sentence-transformers safetensors roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:809 loss:MultipleNegativesRankingLoss dataset:shawhin/ai-job-embedding-finetuning arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers/all-distilroberta-v1 base_model:finetune:sentence-transformers/all-distilroberta-v1 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-distilroberta-v1
datasets:
- shawhin/ai-job-embedding-finetuning
library_name: sentence-transformers
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:809
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Senior Data Analyst Pricing, B2B B2C Pricing Strategies, A/B Testing
    Analysis
  sentences:
  - "Qualifications\n Data Engineering, Data Modeling, and ETL (Extract Transform\
    \ Load) skillsData Warehousing and Data Analytics skillsExperience with data-related\
    \ tools and technologiesStrong problem-solving and analytical skillsExcellent\
    \ written and verbal communication skillsAbility to work independently and remotelyExperience\
    \ with cloud platforms (e.g., AWS, Azure) is a plusBachelor's degree in Computer\
    \ Science, Information Systems, or related field"
  - Skills You BringBachelor’s or Master’s Degree in a technology related field (e.g.
    Engineering, Computer Science, etc.) required with 6+ years of experienceInformatica
    Power CenterGood experience with ETL technologiesSnaplogicStrong SQLProven data
    analysis skillsStrong data modeling skills doing either Dimensional or Data Vault
    modelsBasic AWS Experience Proven ability to deal with ambiguity and work in fast
    paced environmentExcellent interpersonal and communication skillsExcellent collaboration
    skills to work with multiple teams in the organization
  - "experience, an annualized transactional volume of $140 billion in 2023, and approximately\
    \ 3,200 employees located in 12+ countries, Paysafe connects businesses and consumers\
    \ across 260 payment types in over 40 currencies around the world. Delivered through\
    \ an integrated platform, Paysafe solutions are geared toward mobile-initiated\
    \ transactions, real-time analytics and the convergence between brick-and-mortar\
    \ and online payments. Further information is available at www.paysafe.com.\n\n\
    Are you ready to make an impact? Join our team that is inspired by a unified vision\
    \ and propelled by passion.\n\nPosition Summary\n\nWe are looking for a dynamic\
    \ and flexible, Senior Data Analyst, Pricing to support our global Sales and Product\
    \ organizations with strategic planning, analysis, and commercial pricing efforts\
    \ . As a Senior Data Analyst , you will be at the frontier of building our Pricing\
    \ function to drive growth through data and AI-enabled capabilities. This opportunity\
    \ is high visibility for someone hungry to drive the upward trajectory of our\
    \ business and be able to contribute to their efforts in the role in our success.\n\
    \nYou will partner with Product Managers to understand their commercial needs,\
    \ then prioritize and work with a cross-functional team to deliver pricing strategies\
    \ and analytics-based solutions to solve and execute them. Business outcomes will\
    \ include sustainable growth in both revenues and gross profit.\n\nThis role is\
    \ based in Jacksonville, Florida and offers a flexible hybrid work environment\
    \ with 3 days in the office and 2 days working remote during the work week.\n\n\
    Responsibilities\n\n Build data products that power the automation and effectiveness\
    \ of our pricing function, driving better quality revenues from merchants and\
    \ consumers.  Partner closely with pricing stakeholders (e.g., Product, Sales,\
    \ Marketing) to turn raw data into actionable insights. Help ask the right questions\
    \ and find the answers.  Dive into complex pricing and behavioral data sets, spot\
    \ trends and make interpretations.  Utilize modelling and data-mining skills to\
    \ find new insights and opportunities.  Turn findings into plans for new data\
    \ products or visions for new merchant features.  Partner across merchant Product,\
    \ Sales, Marketing, Development and Finance to build alignment, engagement and\
    \ excitement for new products, features and initiatives.  Ensure data quality\
    \ and integrity by following and enforcing data governance policies, including\
    \ alignment on data language. \n\n  Qualifications  \n\n Bachelor’s degree in\
    \ a related field of study (Computer Science, Statistics, Mathematics, Engineering,\
    \ etc.) required.  5+ years of experience of in-depth data analysis role, required;\
    \ preferably in pricing context with B2B & B2C in a digital environment.  Proven\
    \ ability to visualize data intuitively, cleanly and clearly in order to make\
    \ important insights simplified.  Experience across large and complex datasets,\
    \ including customer behavior, and transactional data.  Advanced in SQL and in\
    \ Python, preferred.  Experience structuring and analyzing A/B tests, elasticities\
    \ and interdependencies, preferred.  Excellent communication and presentation\
    \ skills, with the ability to explain complex data insights to non-technical audiences.\
    \ \n\n Life at Paysafe: \n\nOne network. One partnership. At Paysafe, this is\
    \ not only our business model; this is our mindset when it comes to our team.\
    \ Being a part of Paysafe means you’ll be one of over 3,200 members of a world-class\
    \ team that drives our business to new heights every day and where we are committed\
    \ to your personal and professional growth.\n\nOur culture values humility, high\
    \ trust & autonomy, a desire for excellence and meeting commitments, strong team\
    \ cohesion, a sense of urgency, a desire to learn, pragmatically pushing boundaries,\
    \ and accomplishing goals that have a direct business impact.\n\n \n\nPaysafe\
    \ provides equal employment opportunities to all employees, and applicants for\
    \ employment, and prohibits discrimination of any type concerning ethnicity, religion,\
    \ age, sex, national origin, disability status, sexual orientation, gender identity\
    \ or expression, or any other protected characteristics. This policy applies to\
    \ all terms and conditions of recruitment and employment. If you need any reasonable\
    \ adjustments, please let us know. We will be happy to help and look forward to\
    \ hearing from you."
- source_sentence: ETL Pipelines, Apache Spark, AirFlow
  sentences:
  - "Experience as a Product Data Analyst at TGG:Achieving business results as a client\
    \ facing consultant for our clients in various types of engagements within a variety\
    \ of industries.Delivering high quality work to our clients within our technology\
    \ service line. Being part of a collaborative, values-based firm that has a reputation\
    \ for great work and satisfied clients.Working with senior IT leaders to communicate\
    \ strategic goals to their organization, including leading client and internal\
    \ development teams on best practices.\nWhat You Will Work On:Analyze large datasets\
    \ to identify patterns, trends, and opportunities for product optimization.Develop\
    \ and maintain dashboards and reports to track key performance metrics.Collaborate\
    \ with product managers, marketers, and engineers to ideate, prioritize, and implement\
    \ data-driven initiatives.Conduct A/B testing and other statistical analyses to\
    \ evaluate the effectiveness of product changes.Communicate findings and recommendations\
    \ to stakeholders through clear and concise presentations.Contribute analytical\
    \ insights to inform product vision and deliver value.\nWho Will You Work With:Client\
    \ stakeholders ranging from individual contributors to senior executives.A collaborative\
    \ team of consultants that deliver outstanding client service.TGG partners, principals,\
    \ account leaders, managers, and staff supporting you to excel within client projects\
    \ and to achieve your professional development goals.\nExamples of What You Bring\
    \ to the Table:You have strong analysis capabilities and thrive on working collaboratively\
    \ to deliver successful results for clients. You have experience with these technologies:Proficiency\
    \ in SQL and Python for data extraction, manipulation, and analysis.Strong understanding\
    \ of statistical concepts and techniques.Intermediate experience with Tableau,\
    \ Power BI, Adobe Analytics, or similar BI tools.Ability to analyze requirements,\
    \ design, implement, debug, and deploy Cloud Platform services and components.At\
    \ least basic exposure to data science and machine learning methods.Familiarity\
    \ with source control best practices: Define, Setup/Configure, Deploy and Maintain\
    \ source code (e.g. GIT, VisualSafe Source).Ability to develop and schedule processes\
    \ to extract, transform, and store data from these systems: SQL databases, Azure\
    \ cloud services, Google cloud service, Snowflake.4-8 years of relevant experience.Bachelor’s\
    \ degree in Computer Science, Statistics, Economics, Mathematics, or a related\
    \ field; or equivalent combination of education, training, and experience.Analytical\
    \ Product Mindset: Ability to approach problems analytically and derive actionable\
    \ insights from complex datasets, while remaining focused on providing value to\
    \ customers Strategic Thinking: Demonstrated ability to translate data findings\
    \ into strategic, achievable recommendations to drive business outcomes.Communication\
    \ Skills: Excellent verbal and written communication skills.Ability to effectively\
    \ convey technical concepts from technical to non-technical stakeholders and vice-versa.Team\
    \ Player: Proven track record of collaborating effectively with cross-functional\
    \ teams in a fast-paced environment.Adaptability: Have consistently demonstrated\
    \ the ability to bring structure to complex, unstructured environments.Familiarity\
    \ with Agile development methodologies.Ability to adapt to changing priorities\
    \ to thrive in dynamic work environments.\nSalary and Benefits:Nothing is more\
    \ important to us than the well-being of our team. That is why we are proud to\
    \ offer a full suite of competitive health benefits along with additional benefits\
    \ such as: flexible PTO, a professional development stipend and work from home\
    \ stipend, volunteer opportunities, and team social activities.\nSalaries vary\
    \ and are dependent on considerations such as: experience and specific skills/certifications.\
    \ The base plus target bonus total compensation range for this role is $95,000\
    \ - $125,000. Additional compensation beyond this range is available as a result\
    \ of leadership and business development opportunities. Salary details are discussed\
    \ openly during the hiring process. \nWork Environment:TGG is headquartered in\
    \ Portland, Oregon, and has team members living in various locations across the\
    \ United States. Our consultants must have the ability to travel and to work remotely\
    \ or onsite. Each engagement has unique conditions, and we work collaboratively\
    \ to meet both our client and team's needs regarding onsite and travel requirements.\
    \ \nWhy The Gunter Group:TGG was created to be different, to be relational, to\
    \ be insightful, and to maximize potential for our consultants, our clients, and\
    \ our community. We listen first so we can learn, analyze, and deliver meaningful\
    \ solutions for our clients. Our compass points towards our people and our “Non-Negotiables”\
    \ always. Our driven employees make us who we are — a talented team of leaders\
    \ with deep and diverse professional experience.If you think this role is the\
    \ right fit, please submit your resume and cover letter so we can learn more about\
    \ you. \nThe Gunter Group LLC is"
  - 'Requirements & Day-to-Day:  Design, develop, and support scalable data processing
    pipelines using Apache Spark and Java/Scala. Lead a talented team and make a significant
    impact on our data engineering capabilities. Implement and manage workflow orchestration
    with AirFlow for efficient data processing. Proficiently use SQL for querying
    and data manipulation tasks. Collaborate with cross-functional teams to gather
    requirements and ensure alignment with data engineering solutions.  Essential
    Criteria:  a bachelor’s degree in computer science or another relevant discipline,
    and a minimum of five years of relevant experience in data engineering. Solid
    experience with Apache Spark for large-scale data processing. Proficiency in Java
    or Scala programming languages. Strong knowledge of AirFlow for workflow orchestration.
    Proficient in SQL for data querying and manipulation.'
  - 'experienced Data Engineer to join our world leading footwear client. The ideal
    candidate will have 6-7 years of relevant experience, with a focus on practical
    application in AWS tech stack. Experience with Databricks, Spark, and Python for
    coding is essential.


    W2 ONLY, NO C2C*



    Key Qualifications:


    Bachelor’s degree in Computer Science or related field.6-7 years of data engineering
    experience.Proficiency in AWS, Databricks, Spark, and Python.Ability to work in
    complex environments with diverse projects.Strong communication and collaboration
    skills.



    Mainz Brady Group is a technology staffing firm with offices in California, Oregon
    and Washington. We specialize in Information Technology and Engineering placements
    on a Contract, Contract-to-hire and Direct Hire basis. Mainz Brady Group is the
    recipient of multiple annual Excellence Awards from the Techserve Alliance, the
    leading association for IT and engineering staffing firms in the U.S.


    Mainz Brady Group is'
- source_sentence: dialog evaluation processes, speech interaction analysis, data
    collection conventions
  sentences:
  - experience with the Refactor the Macro code from local Python/R implementation
    to Databricks (Python/Pyspark) Analytical expert who utilize his/her skills in
    both technology and social science to find trends and manage data.They use industry
    knowledge, contextual understanding, skepticism of existing assumptions – to uncover
    solutions to business challengesCollecting, analysis and clean up dataCreating
    algorithms for processing catalog products using different data sourcesExperimenting
    with different models and neural networks, creating model ensemblesCreating a
    workflow for publishing algorithms to productionStrong skills in a machine and/or
    deep learning algorithms, data cleaning, feature extraction, and generationDemonstrated
    computational skills and experience with PythonExperience executing and presenting
    independent analysis Must have skills:Python(Programming Language)R (Programming
    Language)PySparkDatabricks
  - 'requirements. You will work closely with cross-functional teams to develop and
    implement data processing solutions that align with business needs. Additionally,
    you will be responsible for ensuring the quality and integrity of data while optimizing
    performance and ensuring data security. The successful candidate must have at
    least 5 years of experience in data engineering, with a strong focus on Azure
    Databricks and Azure Data Factory. You should be able to design and develop efficient
    data processing pipelines and should be proficient in SQL queries. Experience
    in JIRA is a must. Must Have below skills:• SQL Quires • SSIS• Data Factory• Databricks•
    JIRA.


    Thanks & RegardsJoshuaDelivery Manager'
  - "experience with speech interfaces Lead and evaluate changing dialog evaluation\
    \ conventions, test tooling developments, and pilot processes to support expansion\
    \ to new data areas Continuously evaluate workflow tools and processes and offer\
    \ solutions to ensure they are efficient, high quality, and scalable Provide expert\
    \ support for a large and growing team of data analysts Provide support for ongoing\
    \ and new data collection efforts as a subject matter expert on conventions and\
    \ use of the data Conduct research studies to understand speech and customer-Alexa\
    \ interactions Assist scientists, program and product managers, and other stakeholders\
    \ in defining and validating customer experience metrics\n\nWe are open to hiring\
    \ candidates to work out of one of the following locations:\n\nBoston, MA, USA\
    \ | Seattle, WA, USA\n\nBasic Qualifications\n\n 3+ years of data querying languages\
    \ (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software\
    \ (e.g. R, SAS, Matlab, etc.) experience 2+ years of data scientist experience\
    \ Bachelor's degree Experience applying theoretical models in an applied environment\n\
    \nPreferred Qualifications\n\n Experience in Python, Perl, or another scripting\
    \ language Experience in a ML or data scientist role with a large technology company\
    \ Master's degree in a quantitative field such as statistics, mathematics, data\
    \ science, business analytics, economics, finance, engineering, or computer science\n\
    \nAmazon is committed to a diverse and inclusive workplace. Amazon is \n\nOur\
    \ compensation reflects the cost of labor across several US geographic markets.\
    \ The base pay for this position ranges from $111,600/year in our lowest geographic\
    \ market up to $212,800/year in our highest geographic market. Pay is based on\
    \ a number of factors including market location and may vary depending on job-related\
    \ knowledge, skills, and experience. Amazon is a total compensation company. Dependent\
    \ on the position offered, equity, sign-on payments, and other forms of compensation\
    \ may be provided as part of a total compensation package, in addition to a full\
    \ range of medical, financial, and/or other benefits. For more information, please\
    \ visit https://www.aboutamazon.com/workplace/employee-benefits. This position\
    \ will remain posted until filled. Applicants should apply via our internal or\
    \ external career site.\n\n\nCompany - Amazon.com Services LLC\n\nJob ID: A2610750"
- source_sentence: Senior Data Analyst with expertise in Power BI, data governance,
    and statistical analysis
  sentences:
  - 'qualifications and experience.

    RESPONSIBILITIESData Analysis and Insights: Utilize advanced data analysis techniques
    to extract insights from large datasets, identify trends, patterns, and correlations,
    and translate findings into actionable recommendations for business stakeholders.
    Develop predictive models, algorithms, and data visualization tools to support
    decision-making processes, optimize business performance, and drive strategic
    initiatives.Strategy Development: Collaborate with senior leadership and key stakeholders
    to develop data-driven strategies and roadmaps that align with business objectives
    and drive innovation across the organization. Conduct market research, competitive
    analysis, and industry benchmarking to identify opportunities for growth, differentiation,
    and competitive advantage.Technology Engineering: Design, develop, and implement
    technology solutions and platforms to support data analytics, reporting, and automation
    initiatives, leveraging tools and technologies such as SQL, Python, R, Tableau,
    Power BI, and cloud-based platforms. Architect and maintain data infrastructure,
    databases, and systems to ensure scalability, reliability, and security of data
    assets.Cross-Functional Collaboration: Partner with cross-functional teams, including
    IT, Marketing, Operations, and Finance, to gather requirements, define solution
    specifications, and ensure successful implementation and adoption of data-driven
    initiatives. Provide technical guidance, training, and support to stakeholders
    to enable self-service analytics and empower data-driven decision-making throughout
    the organization.Performance Monitoring and Optimization: Monitor and analyze
    the performance of data analytics solutions and technology platforms, identifying
    opportunities for optimization, scalability, and continuous improvement. Implement
    best practices, standards, and governance frameworks to ensure data integrity,
    privacy, and compliance with regulatory requirements.

    REQUIREMENTSOccasionally lift and/or move up to 25 lbs. Ability to understand
    and follow instructions in English.Ability to sit for extended periods of time,
    twist, bend, sit, walk use hands to twist, handle or feel objects, tools or controls,
    such as computer mouse, computer keyboard, calculator, stapler, telephone, staple
    puller, etc., reach with hands and arms, balance, stoop, kneel, talk or hear.Specific
    vision abilities required by the job include close vision, distance vision, peripheral
    vision, depth perception and the ability to adjust focus.

    QUALIFICATIONSBachelor''s degree in Computer Science, Data Science, Information
    Systems, or related field; Master''s degree or relevant certification preferred.X
    years of experience in data analysis, strategy development, and technology engineering
    roles, preferably in the financial services or banking industry.Strong proficiency
    in data analysis tools and programming languages, such as SQL, Python, R, and
    experience with data visualization tools such as Tableau or Power BI.Solid understanding
    of data modeling, database design, and data warehousing principles, with experience
    working with relational databases and cloud-based platforms.Proven track record
    of developing and implementing data-driven strategies and technology solutions
    that drive business value and operational efficiency.Excellent communication,
    problem-solving, and stakeholder management skills.Ability to work independently
    as well as collaboratively in a fast-paced, dynamic environment. Strong analytical
    mindset, attention to detail, and a passion for leveraging data and technology
    to solve complex business challenges.

    ABOUT STEARNS BANKStearns Bank is a leading financial institution dedicated to
    leveraging cutting-edge technology and data analytics to provide innovative banking
    solutions. With a commitment to excellence and continuous improvement, Stearns
    Bank offers a dynamic and collaborative work environment for professionals seeking
    to make a significant impact in the finance and technology sectors.

    WHY JOIN STEARNS BANK?Opportunity to work at the intersection of finance, technology,
    and data analytics, driving innovation and shaping the future of banking. Collaborative
    and inclusive work culture that values diversity, creativity, and continuous learning.
    Competitive compensation package with comprehensive benefits and opportunities
    for professional development and advancement. Make a meaningful impact by leveraging
    your expertise to drive data-driven decision-making and technology innovation,
    contributing to the success and growth of Stearns Bank.Note: The above job description
    is intended to outline the general nature and level of work being performed by
    individuals assigned to this position. It is not intended to be construed as an
    exhaustive list of responsibilities, duties, and skills required. Management reserves
    the right to modify, add, or remove duties as necessary to meet business needs.

    EQUAL OPPORTUNITY EMPLOYER /AFFIRMATIVE ACTION PLANWe are'
  - 'experience with kubernetes operating knowledge.Working with data pipelines and
    experience with Spark and FlinkExcellent communication skillsNice to have:Programming
    experience in Scala, Java, and PythonKnowledge on Machine Learning (Client)

    Job description:The client seeks to improve products by using data as the voice
    of our customers. We are looking for engineers to collaborate with users of our
    infrastructure and architect new pipelines to improve the user onboarding experience.
    As part of this group, you will work with petabytes of data daily using diverse
    technologies like Spark, Flink, Kafka, Hadoop, and others. You will be expected
    to effectively partner with upstream engineering teams and downstream analytical
    & product consumers. Experience:10+ YOE, with 5+ years of experience designing
    and implementing batch or real-time data pipelinesHands-on experience on batch
    processing (Spark, Presto, Hive) or streaming (Flink, Beam, Spark Streaming)Experience
    in AWS and knowledge in its ecosystem. Experience in scaling and operating kubernetes.Excellent
    communication skills is a must, experience working with customers directly to
    explain how they would use the infrastructure to build complex data pipelinesProven
    ability to work in an agile environment, flexible to adapt to changesAble to work
    independently, research on possible solutions to unblock customerProgramming experience
    in Scala, Java, or PythonFast learner and experience with other common big data
    open source technologies is a big plusKnowledge on machine learning (Client) is
    a nice-to-have'
  - 'skills and proficiency/expertise in analytical tools including PowerBI development,
    Python, coding, Excel, SQL, SOQL, Jira, and others.Must be detail oriented, focused
    on excellent quality deliverables and able to analyze data quickly using multiple
    tools and strategies including creating advanced algorithms.Position serves as
    a critical member of data integrity team within digital solutions group and supplies
    detailed analysis on key data elements that flow between systems to help design
    governance and master data management strategies and ensure data cleanliness.

    Requirements:5 to 8 years related experience preferred. Bachelor''s degree preferred.Power
    BIPythonSQL/SOQLJiraExcel'
- source_sentence: data integrity governance PowerBI development Juno Beach
  sentences:
  - "skills: 2-5 y of exp with data analysis/ data integrity/ data governance; PowerBI\
    \ development; Python; SQL, SOQL\n\nLocation: Juno Beach, FL\nPLEASE SEND LOCAL\
    \ CANDIDATES ONLY\n\nSeniority on the skill/s required on this requirement: Mid.\n\
    \nEarliest Start Date: ASAP\n\nType: Temporary Project\n\nEstimated Duration:\
    \ 12 months with possible extension(s)\n\nAdditional information: The candidate\
    \ should be able to provide an ID if the interview is requested. The candidate\
    \ interviewing must be the same individual who will be assigned to work with our\
    \ client. \nRequirements:• Availability to work 100% at the Client’s site in Juno\
    \ Beach, FL (required);• Experience in data analysis/ data integrity/ data governance;•\
    \ Experience in analytical tools including PowerBI development, Python, coding,\
    \ Excel, SQL, SOQL, Jira, and others.\n\nResponsibilities include but are not\
    \ limited to the following:• Analyze data quickly using multiple tools and strategies\
    \ including creating advanced algorithms;• Serve as a critical member of data\
    \ integrity team within digital solutions group and supplies detailed analysis\
    \ on key data elements that flow between systems to help design governance and\
    \ master data management strategies and ensure data cleanliness."
  - "skills and expertise, experience and other relevant factors (salary may be adjusted\
    \ based on geographic location)\n\n What does it mean to work at Armstrong?\n\n\
    It means being immersed in a supportive culture that recognizes you as a key player\
    \ in Armstrong's future. We are a large company with a local feel, where you will\
    \ get to know and collaborate with leadership and your colleagues across the company.\n\
    \nBy joining us, you'll have the opportunity to make the most of your potential.\
    \ Alongside a competitive remuneration package, you will receive:\n\nA benefits\
    \ package including: medical, dental, prescription drug, life insurance, 401k\
    \ match, long-term disability coverage, vacation and sick time, product discount\
    \ programs and many more.Personal development to grow your career with us based\
    \ on your strengths and interests.A working culture that balances individual achievement\
    \ with teamwork and collaboration. We draw on each other's strengths and allow\
    \ for different work styles to build engagement and satisfaction to deliver results.\
    \ \n\n\nAs a Data Scientist, you will leverage cutting-edge generative AI techniques\
    \ to extract structured data from diverse document types. From there, you will\
    \ build models that understand context, domain-specific jargon and generate documents.\
    \ The output of your work will enable long-term strategic advantages for the company.\n\
    \nEssential Duties and Responsibilities include the following. Other duties may\
    \ be assigned.\n\nBuilding AI/ML features to evaluate document quality, account\
    \ loyalty, market trends, etc.Constructing supervised learning datasetsWriting\
    \ robust and testable codeDefining and overseeing regular updates to improve precision\
    \ as the company’s challenges and data evolveCultivating strong collaborations\
    \ with teammates and stakeholdersSharing technical solutions and product ideas\
    \ with the team through design/code reviews and weekly meetings\n\n\nQualifications\n\
    \nTo perform this job successfully, an individual must be able to perform each\
    \ essential duty satisfactorily. The requirements listed below are representative\
    \ of the knowledge, skill, and/or ability required. Reasonable accommodations\
    \ may be made to enable individuals with disabilities to perform the essential\
    \ functions.\n\nExperience transforming natural language data into useful features\
    \ using NLP techniques to feed classification algorithmsAbility to work with dashboarding\
    \ and visualization software such as Tableau or Power BIKnowledge of software\
    \ versioning control repositories such as GitHubAbility to translate data insights\
    \ into actionable items and communicate findings in a simplistic wayExperience\
    \ with generative AI would be a plus Enthusiasm for learning new things and going\
    \ deep into detailed data analysisWorkflow flexibility, team player, and strong\
    \ collaboration skills\n\n\nEducation And/or Experience\n\nBS in Computer Science,\
    \ Statistics or Applied Mathematics or equivalent years of experience2+ years\
    \ in software development, statistical modeling, and machine learning2+ years\
    \ of experience in an analytical field using tools such as Python, R, SAS, MatlabFamiliarity\
    \ with SQL or other querying languages is preferred\n\n\nWhy should you join Armstrong\
    \ World Industries?\n\nArmstrong World Industries (AWI) is a leader in the design\
    \ and manufacture of innovative commercial and residential ceiling, wall and suspension\
    \ system solutions in the Americas. With approximately $1B in revenue, AWI has\
    \ about 2,800 employees and a manufacturing network of fifteen facilities in North\
    \ America.\n\nAt home, at work, in healthcare facilities, classrooms, stores,\
    \ or restaurants, we offer interior solutions that help to enhance comfort, save\
    \ time, improve building efficiency and overall performance, and create beautiful\
    \ spaces.\n\nFor more than 150 years, we have built our business on trust and\
    \ integrity. It set us apart then, and it sets us apart now, along with our ability\
    \ to collaborate with and innovate for the people we're here to serve - our customers,\
    \ our shareholders, our communities and our employees.\n\nWe are committed to\
    \ developing new and sustainable ceiling solutions, with design and performance\
    \ possibilities that make a positive difference in spaces where we live, work,\
    \ learn, heal and play. It's an exciting, rewarding business to be in, and we're\
    \ committed to continue to grow and prosper for the benefit of all of our stakeholders.\
    \ We hope you join us.\n\nOur Sustainability Ambition\n\n\"Bringing our Purpose\
    \ to Life\" - lead a transformation in the design and building of spaces fit for\
    \ today and tomorrow.\n\nWe are committed to:\n\nEngaging a diverse, purpose-driven\
    \ workforce;Transforming buildings from structures that shelter into structures\
    \ that serve and preserve the health and well-being of people and planet;Pursuing\
    \ sustainable, innovative solutions for spaces where we live, work, learn heal\
    \ and play;Being a catalyst for change with all of our stakeholders; andMaking\
    \ a positive difference in the environments and communities we impact.\n\n\nArmstrong\
    \ is committed to engaging a diverse, purpose-driven workforce. As part of our\
    \ dedication to diversity, AWI is committed to \n\nCome and build your future\
    \ with us and apply today!"
  - "QualificationsAdvanced degree (MS with 5+ years of industry experience, or Ph.D.)\
    \ in Computer Science, Data Science, Statistics, or a related field, with an emphasis\
    \ on AI and machine learning.Proficiency in Python and deep learning libraries,\
    \ notably PyTorch and Hugging Face, Lightning AI, evidenced by a history of deploying\
    \ AI models.In-depth knowledge of the latest trends and techniques in AI, particularly\
    \ in multivariate time-series prediction for financial applications.Exceptional\
    \ communication skills, capable of effectively conveying complex technical ideas\
    \ to diverse audiences.Self-motivated, with a collaborative and solution-oriented\
    \ approach to problem-solving, comfortable working both independently and as part\
    \ of a collaborative team.\n\nCompensationThis role is compensated with equity\
    \ until the product expansion and securing of Series A investment. Cash-based\
    \ compensation will be determined after the revenue generation has been started.\
    \ As we grow, we'll introduce additional benefits, including performance bonuses,\
    \ comprehensive health insurance, and professional development opportunities.\
    \ \nWhy Join BoldPine?\nInfluence the direction of financial market forecasting,\
    \ contributing to groundbreaking predictive models.Thrive in an innovative culture\
    \ that values continuous improvement and professional growth, keeping you at the\
    \ cutting edge of technology.Collaborate with a dedicated team, including another\
    \ technical expert, setting new benchmarks in AI-driven financial forecasting\
    \ in a diverse and inclusive environment.\nHow to Apply\nTo join a team that's\
    \ redefining financial forecasting, submit your application, including a resume\
    \ and a cover letter. At BoldPine, we're committed to creating a diverse and inclusive\
    \ work environment and encouraging applications from all backgrounds. Join us,\
    \ and play a part in our mission to transform financial predictions."
model-index:
- name: SentenceTransformer based on sentence-transformers/all-distilroberta-v1
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: ai job validation
      type: ai-job-validation
    metrics:
    - type: cosine_accuracy
      value: 0.9900990099009901
      name: Cosine Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: ai job test
      type: ai-job-test
    metrics:
    - type: cosine_accuracy
      value: 1.0
      name: Cosine Accuracy
---

# SentenceTransformer based on sentence-transformers/all-distilroberta-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) on the [ai-job-embedding-finetuning](https://huggingface.co/datasets/shawhin/ai-job-embedding-finetuning) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

Resulting model from example code for fine-tuning an embedding model for AI job search.

Links
- [GitHub Repo](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/fine-tuning-embeddings)
- [Video link](https://youtu.be/hOLBrIjRAj4)
- [Blog link](https://shawhin.medium.com/fine-tuning-text-embeddings-f913b882b11c)

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) <!-- at revision 8d88b92a34345fd6a139aa47768c9881720006ce -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [ai-job-embedding-finetuning](https://huggingface.co/datasets/shawhin/ai-job-embedding-finetuning)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("shawhin/distilroberta-ai-job-embeddings")
# Run inference
sentences = [
    'data integrity governance PowerBI development Juno Beach',
    'skills: 2-5 y of exp with data analysis/ data integrity/ data governance; PowerBI development; Python; SQL, SOQL\n\nLocation: Juno Beach, FL\nPLEASE SEND LOCAL CANDIDATES ONLY\n\nSeniority on the skill/s required on this requirement: Mid.\n\nEarliest Start Date: ASAP\n\nType: Temporary Project\n\nEstimated Duration: 12 months with possible extension(s)\n\nAdditional information: The candidate should be able to provide an ID if the interview is requested. The candidate interviewing must be the same individual who will be assigned to work with our client. \nRequirements:• Availability to work 100% at the Client’s site in Juno Beach, FL (required);• Experience in data analysis/ data integrity/ data governance;• Experience in analytical tools including PowerBI development, Python, coding, Excel, SQL, SOQL, Jira, and others.\n\nResponsibilities include but are not limited to the following:• Analyze data quickly using multiple tools and strategies including creating advanced algorithms;• Serve as a critical member of data integrity team within digital solutions group and supplies detailed analysis on key data elements that flow between systems to help design governance and master data management strategies and ensure data cleanliness.',
    "QualificationsAdvanced degree (MS with 5+ years of industry experience, or Ph.D.) in Computer Science, Data Science, Statistics, or a related field, with an emphasis on AI and machine learning.Proficiency in Python and deep learning libraries, notably PyTorch and Hugging Face, Lightning AI, evidenced by a history of deploying AI models.In-depth knowledge of the latest trends and techniques in AI, particularly in multivariate time-series prediction for financial applications.Exceptional communication skills, capable of effectively conveying complex technical ideas to diverse audiences.Self-motivated, with a collaborative and solution-oriented approach to problem-solving, comfortable working both independently and as part of a collaborative team.\n\nCompensationThis role is compensated with equity until the product expansion and securing of Series A investment. Cash-based compensation will be determined after the revenue generation has been started. As we grow, we'll introduce additional benefits, including performance bonuses, comprehensive health insurance, and professional development opportunities. \nWhy Join BoldPine?\nInfluence the direction of financial market forecasting, contributing to groundbreaking predictive models.Thrive in an innovative culture that values continuous improvement and professional growth, keeping you at the cutting edge of technology.Collaborate with a dedicated team, including another technical expert, setting new benchmarks in AI-driven financial forecasting in a diverse and inclusive environment.\nHow to Apply\nTo join a team that's redefining financial forecasting, submit your application, including a resume and a cover letter. At BoldPine, we're committed to creating a diverse and inclusive work environment and encouraging applications from all backgrounds. Join us, and play a part in our mission to transform financial predictions.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Triplet

* Datasets: `ai-job-validation` and `ai-job-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | ai-job-validation | ai-job-test |
|:--------------------|:------------------|:------------|
| **cosine_accuracy** | **0.9901**        | **1.0**     |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### ai-job-embedding-finetuning

* Dataset: [ai-job-embedding-finetuning](https://huggingface.co/datasets/shawhin/ai-job-embedding-finetuning) at [c86ac36](https://huggingface.co/datasets/shawhin/ai-job-embedding-finetuning/tree/c86ac36bb69d9cfa0f85968382b58e1be707f85b)
* Size: 809 training samples
* Columns: <code>query</code>, <code>job_description_pos</code>, and <code>job_description_neg</code>
* Approximate statistics based on the first 809 samples:
  |         | query                                                                             | job_description_pos                                                                 | job_description_neg                                                                 |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 15.02 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 348.14 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 351.24 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | query                                                                    | job_description_pos                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | job_description_neg                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
  |:-------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Data engineering Azure cloud Apache Spark Kafka</code>             | <code>Skills:Proven experience in data engineering and workflow development.Strong knowledge of Azure cloud services.Proficiency in Apache Spark and Apache Kafka.Excellent programming skills in Python/Java.Hands-on experience with Azure Synapse, DataBricks, and Azure Data Factory.<br>Nice To Have Skills:Experience with BI Tools such as Tableau or Power BI.Familiarity with Terraform for infrastructure as code.Knowledge of Git Actions for CI/CD pipelines.Understanding of database design and architecting principles.Strong communication skills and ability to manage technical projects effectively.</code>                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>requirements, and assist in data structure implementation planning for innovative data visualization, predictive modeling, and advanced analytics solutions.* Unfortunately, we cannot accommodate Visa Sponsorship for this role at this time.<br>ESSENTIAL JOB FUNCTIONS<br>Mine data covering a wide range of information from customer profile to transaction details to solve risk problems that involve classification, clustering, pattern analysis, sampling and simulations.Apply strong data science expertise and systems analysis methodology to help guide solution analysis, working closely with both business and technical teams, with consideration of both technical and non-technical implications and trade-offs.Carry out independent research and innovation in new content, ML, and technological domains. Trouble shooting any data, system and flow challenges while maintaining clearly defined strategy execution.Extract data from various data sources; perform exploratory data analysis, cleanse, transform, a...</code>             |
  | <code>Databricks, Medallion architecture, ETL processes</code>           | <code>experience with Databricks, PySpark, SQL, Spark clusters, and Jupyter Notebooks.- Expertise in building data lakes using the Medallion architecture and working with delta tables in the delta file format.- Familiarity with CI/CD pipelines and Agile methodologies, ensuring efficient and collaborative development practices.- Strong understanding of ETL processes, data modeling, and data warehousing principles.- Experience with data visualization tools like Power BI is a plus.- Knowledge of cybersecurity data, particularly vulnerability scan data, is preferred.- Bachelor's or Master's degree in Computer Science, Information Systems, or a related field.<br>requirements and deliver effective solutions aligned with Medallion architecture principles.- Ensure data quality and implement robust data governance standards, leveraging the scalability and efficiency offered by the Medallion architecture.- Design and implement ETL processes, including data cleansing, transformation, and integration, opti...</code> | <code>experience with a minimum of 0+ years of experience in a Computer Science or Data Management related fieldTrack record of implementing software engineering best practices for multiple use cases.Experience of automation of the entire machine learning model lifecycle.Experience with optimization of distributed training of machine learning models.Use of Kubernetes and implementation of machine learning tools in that context.Experience partnering and/or collaborating with teams that have different competences.The role holder will possess a blend of design skills needed for Agile data development projects.Proficiency or passion for learning, in data engineer techniques and testing methodologies and Postgraduate degree in data related field of study will also help. <br><br><br>Desirable for the role<br><br><br>Experience with DevOps or DataOps concepts, preferably hands-on experience implementing continuous integration or highly automated end-to-end environments.Interest in machine learning will also be advan...</code> |
  | <code>Gas Processing, AI Strategy Development, Plant Optimization</code> | <code>experience in AI applications for the Hydrocarbon Processing & Control Industry, specifically, in the Gas Processing and Liquefaction business. Key ResponsibilitiesYou will be required to perform the following:- Lead the development and implementation of AI strategies & roadmaps for optimizing gas operations and business functions- Collaborate with cross-functional teams to identify AI use cases to transform gas operations and business functions (AI Mapping)- Design, develop, and implement AI models and algorithms that solve complex problems- Implement Gen AI use cases to enhance natural gas operations and optimize the Gas business functions- Design and implement AI-enabled plant optimizers for efficiency and reliability- Integrate AI models into existing systems and applications- Troubleshoot and resolve technical issues related to AI models and deployments- Ensure compliance with data privacy and security regulations- Stay up-to-date with the latest advancements in AI and machine lea...</code>    | <code>QualificationsAbility to gather business requirements and translate them into technical solutionsProven experience in developing interactive dashboards and reports using Power BI (3 years minimum)Strong proficiency in SQL and PythonStrong knowledge of DAX (Data Analysis Expressions)Experience working with APIs inside of Power BIExperience with data modeling and data visualization best practicesKnowledge of data warehousing concepts and methodologiesExperience in data analysis and problem-solvingExcellent communication and collaboration skillsBachelor's degree in Computer Science, Information Systems, or a related fieldExperience with cloud platforms such as Azure or AWS is a plus<br>HoursApproximately 15 - 20 hours per week for 3 months with the opportunity to extend the contract further</code>                                                                                                                                                                                                                                |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### ai-job-embedding-finetuning

* Dataset: [ai-job-embedding-finetuning](https://huggingface.co/datasets/shawhin/ai-job-embedding-finetuning) at [c86ac36](https://huggingface.co/datasets/shawhin/ai-job-embedding-finetuning/tree/c86ac36bb69d9cfa0f85968382b58e1be707f85b)
* Size: 101 evaluation samples
* Columns: <code>query</code>, <code>job_description_pos</code>, and <code>job_description_neg</code>
* Approximate statistics based on the first 101 samples:
  |         | query                                                                             | job_description_pos                                                                  | job_description_neg                                                                  |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               | string                                                                               |
  | details | <ul><li>min: 8 tokens</li><li>mean: 15.39 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 381.37 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 320.86 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | query                                                                   | job_description_pos                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | job_description_neg                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
  |:------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Data Engineer Snowflake ETL big data processing</code>            | <code>experience. But most of all, we’re backed by a culture of respect. We embrace authenticity and inspire people to thrive.<br><br>The CDE Data Engineer will join the Content Delivery Engineering team, within the Global Video Engineering organization at NBCUniversal. The CDE Data Engineer will be responsible for implementing and maintaining systems that ingest, process, and store vast amounts of data from internal systems and external partner systems. These data systems must be scalable, robust, and within budget. In this role, the CDE Data Engineer will work with a variety of technologies that support the building of meaningful models, alerts, reports, and visualizations from vast quantities of data.<br><br>Responsibilities Include, But Are Not Limited To<br><br>Development of data systems and pipelinesAssist in cleansing, discretization, imputation, selection, generalization etc. to create high quality features for the modeling processWork with business stakeholders to define business requirements includ...</code> | <code>Skills Looking For:- The project involves creating a unified data structure for Power BI reporting.- Candidate would work on data architecture and unifying data from various sources.- Data engineering expertise, including data modeling and possibly data architecture.- Proficiency in Python, SQL, and DAX.- Work with AWS data, and data storage.- Experience with cloud platforms like AWS is preferred.- Familiarity with Microsoft Power Automate and Microsoft Fabric is a plus.- Collaborating with users to understand reporting requirements for Power BI. Must be good at using Power BI tools (creating dashboards); excellent Excel skills.- Supply chain background preferred.<br>Education and Level of Experience:- Bachelor's degree (quantitative learnings preferred- data analytics, statistics, computer science, math) with 3 to 5 years of experience.- Must have recent and relevant experience.<br>Top 3 Skills:- Data engineering, including data modeling and data architecture.- Proficiency in Python, SQL, a...</code>    |
  | <code>GenAI applications, NLP model development, MLOps pipelines</code> | <code>experience building enterprise level GenAI applications, designed and developed MLOps pipelines . The ideal candidate should have deep understanding of the NLP field, hands on experience in design and development of NLP models and experience in building LLM-based applications. Excellent written and verbal communication skills with the ability to collaborate effectively with domain experts and IT leadership team is key to be successful in this role. We are looking for candidates with expertise in Python, Pyspark, Pytorch, Langchain, GCP, Web development, Docker, Kubeflow etc. Key requirements and transition plan for the next generation of AI/ML enablement technology, tools, and processes to enable Walmart to efficiently improve performance with scale. Tools/Skills (hands-on experience is must):• Ability to transform designs ground up and lead innovation in system design• Deep understanding of GenAI applications and NLP field• Hands on experience in the design and development of NLP mode...</code>                   | <code>skills, education, experience, and other qualifications. <br>Featured Benefits:<br>Medical Insurance in compliance with the ACA401(k)Sick leave in compliance with applicable state, federal, and local laws<br>Description: Responsible for performing routine and ad-hoc analysis to identify actionable business insights, performance gaps and perform root cause analysis. The Data Analyst will perform in-depth research across a variety of data sources to determine current performance and identify trends and improvement opportunities. Collaborate with leadership and functional business owners as well as other personnel to understand friction points in data that cause unnecessary effort, and recommend gap closure initiatives to policy, process, and system.  Qualification: Minimum of three (3) years of experience in data analytics, or working in a data analyst environment.Bachelor’s degree in Data Science, Statistics, Applied Math, Computer Science, Business, or related field of study from an accredited ...</code> |
  | <code>data engineering ETL cloud platforms data security</code>         | <code>experience. While operating within the Banks risk appetite, achieves results by consistently identifying, assessing, managing, monitoring, and reporting risks of all types.<br><br>ESSENTIAL DUTIES AND SKILLS, AND ABILITIES REQUIRED:<br><br> Bachelors degree in Computer Science/Information Systems or equivalent combination of education and experience. Must be able to communicate ideas both verbally and in writing to management, business and IT sponsors, and technical resources in language that is appropriate for each group. Fundamental understanding of distributed computing principles Knowledge of application and data security concepts, best practices, and common vulnerabilities. Conceptual understanding of one or more of the following disciplines preferred big data technologies and distributions, metadata management products, commercial ETL tools, Bi and reporting tools, messaging systems, data warehousing, Java (language and run time environment), major version control systems, continuous integra...</code>       | <code>experience.<br><br>We are looking for a highly energetic and collaborative Senior Data Engineer with experience leading enterprise data projects around Business and IT operations. The ideal candidate should be an expert in leading projects in developing and testing data pipelines, data analytics efforts, proactive issue identification and resolution and alerting mechanism using traditional, new and emerging technologies. Excellent written and verbal communication skills and ability to liaise with technologists to executives is key to be successful in this role. <br>• Assembling large to complex sets of data that meet non-functional and functional business requirements• Identifying, designing and implementing internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes• Building required infrastructure for optimal extraction, transformation and loading of data from various data sources using GCP/Azure and S...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | ai-job-validation_cosine_accuracy | ai-job-test_cosine_accuracy |
|:-----:|:----:|:---------------------------------:|:---------------------------:|
| 0     | 0    | 0.8812                            | -                           |
| 1.0   | 51   | 0.9901                            | 1.0                         |


### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0
- PyTorch: 2.5.1
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->