ASSOCIATE DATA SCIENTIST JOB DESCRIPTION

An Associate Data Scientist who goes dark for a month leaves a software product running on stale assumptions: scoring models drift, entity records corrupt, and the customer-facing signals the product depends on stop reflecting reality. This role owns the model development and data pipeline work that keeps a software product's intelligence layer current and trustworthy. It typically sits within a cross-functional product or analytics team, reporting to a senior data scientist or analytics manager, and operates across responsibilities that span raw data up through deployed model output. The work is hard because the source data rarely arrives clean and the business questions rarely arrive structured.

As the Associate Data Scientist, you will own the development and operationalization of machine learning models and data pipelines that transform raw, often unstructured data into scored, structured intelligence used directly in software product decisions. You will collaborate with Data Engineering, Product, and business stakeholders while reporting to a senior data scientist or analytics lead, contributing across a team that ranges from individual model ownership to cross-functional delivery.

Career Impact: Building end-to-end model ownership at the Associate level - from feature engineering through production deployment - establishes the depth that distinguishes a data scientist from a data analyst in the software market.

Business Impact: The models and pipelines you build directly determine what the product surfaces to users, including risk scores, recommendations, and behavioral classifications that drive customer decisions.

Growth Opportunity: The combination of NLP, graph analytics, and ML Ops exposure in this role accelerates the path toward a Senior Data Scientist or Machine Learning Engineer title, two of the highest-demand roles in software product companies.

Company Value: Several employers in this space offer benefits including unlimited PTO, annual travel stipends, Group Medical Insurance, Parental Leave, and an EAP, reflecting a product-team culture that invests in retention.

• Build and operationalize machine learning models, including scoring, classification, and entity resolution, to support product intelligence features.
• Design data processing pipelines that ingest, cleanse, and transform messy or unstructured source data into analysis-ready formats.
• Develop NLP and text analytics solutions to extract structured information from document-heavy or review-heavy datasets.
• Validate and monitor deployed models to detect drift, recalibrate scores, and maintain output accuracy over time.
• Translate unstructured business questions into quantitative problem definitions, then advise on the appropriate algorithmic approach.
• Collaborate with Data Engineering and software engineers to integrate research-driven models into production systems.
• Communicate analytical results and model outputs to non-technical stakeholders in clear, actionable formats.
• Support algorithm enhancement and optimization in response to shifting business requirements or new data sources.

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or equivalent work experience.
• 1 or more years of applied data science experience, with demonstrated ownership of at least one production model or pipeline.
• Proficiency in Python for data manipulation, model development, and production scripting.
• Experience writing SQL for data cleansing, transformation, aggregation, and exploratory analysis.
• Working knowledge of machine learning algorithms including regression, classification, clustering, and ensemble methods.
• Experience building or maintaining ETL or data processing pipelines across structured and unstructured data sources.
• Ability to communicate quantitative findings and model behavior to both technical and non-technical audiences.
• Familiarity with cloud-based computing environments for model training, storage, and deployment.

• Graduate-level training in a quantitative discipline, such as a Master's degree in Statistics, Computer Science, or Operations Research.
• Experience with NLP techniques including text classification, entity recognition, and sentiment analysis within a software product context.
• Exposure to graph data structures or graph-based inference methods for relationship modeling.
• Familiarity with model risk management practices, including score calibration, monitoring frameworks, and version control for model artifacts.

• Model accuracy delta on retraining cycles, measuring how well maintained models stay current against new data.
• Entity resolution precision rate, tracking the share of correctly correlated records across messy or duplicate datasets.
• Pipeline reliability rate, measured as the percentage of scheduled pipeline runs completing without failure or data loss.
• Time from model prototype to production deployment, reflecting the engineer's ability to move work through the full development sequence.
• Stakeholder-reported analytic actionability score, capturing whether model outputs are usable for product or business decisions.
• Typical tools: statistical programming (commonly Python or R); database querying (commonly SQL); cloud platforms (commonly AWS or Azure).

• Base Salary Range: $85,000 to $120,000 annually, varying by market and seniority.
• Bonus: 5% to 10% annual performance bonus typical in software product companies.
• Equity: RSU grants common at Series B and later stage companies; options at earlier stages.
• Health Benefits: Medical, dental, and vision coverage; employer contribution rates vary.
• PTO: 15 to unlimited days depending on company policy; several examples offer unlimited PTO.
• Common Perks: Annual travel stipend, monthly team events, catered meals, EAP, parental leave.

Figures are estimates based on general US market benchmarks and may be outdated. Adjust based on location, company size, and seniority level.

Candidates of all backgrounds are encouraged to apply. Employment decisions are made without regard to race, color, religion, national origin, sex, age, disability, veteran status, genetic information, sexual orientation, gender identity, or any other characteristic protected under applicable federal, state, or local law. Reasonable accommodations are available for qualified individuals with disabilities throughout the hiring process upon request. Final offers are contingent on successful completion of a background check. Candidates must be authorized to work in the United States.

The Associate Data Scientist owns the full data science function at Levelset, building infrastructure, models, and pipelines that turn messy construction-industry data into actionable insights across business and product use cases. Working as the company's first data science hire, the Associate Data Scientist delivers direct impact on users by rating contractor payment risk, resolving entity ambiguity, and surfacing document recommendations through graph-based analysis.

Key Responsibilities

• Move and analyze data across business and product use cases to generate actionable insights.
• Select tools and build infrastructure to support data science initiatives.
• Correlate company entities and resolve ambiguous or duplicate records across messy datasets.
• Summarize user review sentiment and identify common pros and cons for contractors.
• Rate contractor payment risk and present findings in a digestible format for users.
• Distinguish between overlapping construction projects at the same address or property.
• Recommend relevant documents to users based on payment risk patterns observed in similar projects.
• Infer working relationships and project connections from graph-based data.

Required Qualifications

• 1–3 years of experience with Machine Learning, Optimization, Neural Networks, and/or Artificial Intelligence.
• Broad understanding of data science techniques with willingness to go deep on specific topics.
• Experience with data cleansing using various utilities and programming techniques.
• Experience with text analytics, NLP, and entity recognition to convert documents into structured information.
• Experience with data processing and ETL pipelines for analysis and production.
• Proficiency in R, Python, SQL, and Cypher.
• Familiarity with MySQL, Redshift/Postgres, Neo4j, Kafka, RabbitMQ, and the AWS stack.
• Competitive health benefits, monthly company parties, catered lunch every Wednesday, unlimited vacation, and an annual personal travel stipend.

Reporting to the Tech Manager of the Data Collections AI team, the Associate Data Scientist shapes enterprise data solutions by designing flexible, scalable systems and building numerical techniques including linear algebra, machine learning, and optimization into production-ready code. Partnering with analysts and technical stakeholders, the Associate Data Scientist advances investment research outcomes by improving data flow, enforcing agile practices, and driving continuous deployment.

Primary Duties

• Design and develop enterprise solutions that are flexible, scalable, and extensible.
• Improve complex data flow, data structures, and database design for platform migration.
• Serve as a role model for object-oriented design, domain modeling, and agile practices including TDD and CI.
• Build solutions incorporating numerical techniques such as linear algebra, machine learning, statistics, and optimization.
• Develop areas of continuous and automated deployment.
• Introduce and follow good development practices, innovative frameworks, and technology solutions to accelerate business.
• Follow best practices in estimation, planning, reporting, and process improvement daily.

Skills & Qualifications

• Master's degree or above in engineering, computer science, statistics, or a related field preferred.
• Expertise with popular ML algorithms including independent modelling and algorithm derivation; experience with text classification, extraction, and Natural Language Processing.
• Familiarity with mutual fund, fixed income, and equity data is a plus.
• Strong independent analysis and statistics ability; intermediate knowledge of statistical methods desirable.
• Experience with back-end XML, relational, and file-based databases including SQL, Postgres, Redshift, Netezza, and HDFS.
• Familiarity with AWS ecosystem services (Lambda, EC2, RDS, EMR) and automation tools such as Puppet or Chef.
• Expertise with Python and packages including pandas, scikit-learn, TensorFlow, numpy, and NLTK; familiarity with data visualization tools such as Tableau, Shiny, and D3.
• Fluent in both oral and written English.

Sitting at the intersection of advanced analytics and decision management, the Associate Data Scientist applies behavior profiling, statistical techniques, and machine learning algorithms to customer activity data drawn from data warehouses and business processes. Operating across fraud risk, credit risk, and operational decisioning domains, the Associate Data Scientist builds and operationalizes scoring models and expert rules that generate measurable improvements in decision quality.

Duties

• Translate business challenges into data science problems in collaboration with partners.
• Mine and analyze large volumes of internal and external data to derive insights on customer behavior, fraud, and credit risk.
• Design, develop, implement, and monitor scoring models, expert rules, and decision analytics using statistical techniques and machine learning algorithms including regressions, neural networks, and decision trees.
• Conduct independent research and innovation in new content and technological domains to improve decision analytics effectiveness.
• Develop and implement analytic tools and automation processes to improve delivery efficiency of decision management solutions.

Requirements

• Master's, PhD, or equivalent experience in a quantitative field such as Computer Science, Mathematics, Engineering, or Artificial Intelligence.
• Minimum 1 year of relevant work experience.
• High proficiency in Python, R, Java, or Scala and familiarity with relevant ML packages.
• Solid SQL knowledge; experience with big data technologies including Hadoop, MapReduce, PIG, Hive, and Spark is an advantage.
• Innovative with strong analytic acumen; team-oriented, responsible, and delivery-focused.
• Excellent spoken and written English.

Embedded within the data science team, the Associate Data Scientist builds information-rich data products using machine learning, statistical modeling, and data mining techniques to solve defense-related challenges. Working closely with project and product owners, this Associate Data Scientist delivers data integration solutions that isolate non-obvious relationships across disparate sources within funding and schedule constraints.

Core Functions

• Work with the data science team and stakeholders to address business challenges using structured, semi-structured, and unstructured data in a distributed processing environment.
• Design, develop, and program methods and systems to consolidate and analyze diverse data sources for actionable insights.
• Develop and code software programs, algorithms, and automated processes to cleanse, integrate, and evaluate large datasets from multiple sources.
• Prepare and deliver presentations communicating complex quantitative methods clearly and concisely.
• Stay current with technical and industry developments, analytics tools, trends, and best practices.
• Contribute to projects under direction of project and product owners within funding, resourcing, and schedule constraints.

Qualifications & Experience

• Experience with Python, Scala, SAS, Matlab, and R scripting in a Linux environment.
• Experience with Python libraries including pandas, numpy, scipy, and statsmodels.
• Experience applying exploratory data analysis and machine learning algorithms including regression, clustering, decision trees, Markov chains, Monte Carlo, Kalman filters, and neural networks.
• Experience with Jupyter Notebooks for sharing and explaining data analysis.
• Experience with cloud services including AWS, Azure, Google Cloud Platform, and IBM Cloud, and ability to collect data via XML and REST APIs.
• Experience with SQL querying and knowledge of PostgreSQL and MySQL databases.
• Experience visualizing data through tools such as Tableau, ggplot, matplotlib, Plotly, Seaborn, D3, and Leaflet.
• Experience with distributed source control using Git or Mercurial.

A key member of the security analytics team, the Associate Data Scientist builds and deploys analytical software and machine learning models that deliver security functionality and improve operational efficiency through automation. Collaborating across data science and engineering functions, the Associate Data Scientist leads evaluation of emerging AI/ML technologies and manages model risks in line with Model Risk Management requirements.

Functions

• Build, design, engineer, and develop analytical software and services that deliver security functionality and improve efficiency through automation as part of the security analytics team.
• Apply information retrieval, data analytics, and statistical modeling techniques to build machine learning models for cybersecurity use cases.
• Evaluate new data sources and emerging AI/ML technologies and drive adoption in production.
• Support and optimize existing analytical models and products.
• Provide model operation support including monitoring and score calibration.
• Manage model risks in line with Model Risk Management requirements.

Experience & Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, CIS/MIS, Cybersecurity, Business, or a related field, or minimum 6 months of work experience.
• Strong software engineering skills required; 1,000+ lines of prior code expected.
• Knowledge of SQL required; machine learning and cybersecurity knowledge are a plus.
• Go preferred for software engineering, with Java considered; Python preferred for ML work, with familiarity with Keras, PyTorch, and scikit-learn a plus.
• Proficiency with Git and Linux/Mac environments required.
• Strong ability to collaborate, take on challenges, and navigate ambiguity; highly driven, resourceful, results-oriented, with strong analytical and problem-solving skills.

Revenue growth, cost optimization, and customer intimacy at Travelport depend on the Associate Data Scientist II, who delivers statistical and machine learning models from internal and external data to provide business solutions and insights across matrixed project teams. Based within the data and analytics function and collaborating with COE Architecture and data governance resources, this Associate Data Scientist II ensures that model development, validation, and implementation processes are scalable, efficient, and compliant.

Accountabilities

• Participate in data science projects using domain knowledge to support revenue growth, cost optimization, and customer intimacy across Travelport.
• Perform data cleansing, transformation, feature engineering, visualization, and data mining.
• Establish scalable, efficient, automated processes for model development, validation, implementation, and large-scale data analysis.
• Develop new insights from available data using scripting languages and DBMS tools.
• Deliver statistical and machine learning models from internal and external data to provide business solutions and insights.
• Contribute effectively to matrixed project teams including onshore and offshore resources to meet project deliverables.
• Proactively identify improvement opportunities through systematic measurement and analysis.
• Liaise with stakeholders to understand business requirements; communicate analytical results in a meaningful and actionable way.
• Collaborate with COE Architecture and data governance resources to ensure compliance, access management, and data quality standards.

Technical Qualifications

• Bachelor's degree in a quantitative field required; advanced degree preferred.
• Domain knowledge and expertise in at least one relevant application or product area.
• Experience implementing mathematical modeling for predictive analysis and/or optimization using R, Python, or similar tools.
• Background in mathematics, operations research, statistics, and/or optimization with quantitative analysis experience working with large, complex data systems.
• Advanced knowledge of at least one programming language (R, Python, Java); working knowledge of DBMS query languages and statistical software packages (R, SAS, SPSS).
• Basic knowledge of big data technologies (Spark, Hadoop), data visualization tools (GGplot, Qlikview), and machine learning and deep learning frameworks.
• Good knowledge of Microsoft Office (Word, Excel, PowerPoint), Visio, and Microsoft Project/PPM.
• Productive with infrequent guidance; beginning to be self-directing and plans activities and tasks independently.

As the Associate Data Scientist, this role leads the delivery of advanced analytics and machine learning solutions focused on customer lifecycle management, campaign optimization, and personalization as an integral part of major business change programs. The digital analytics team relies on this work to translate unstructured business questions into quantitative models, put machine learning into production, and foster a data- and insight-driven culture across business units.

Activities

• Generate actionable analytics and insights as an integral part of major business change programs, focusing on customer lifecycle management, campaign optimization, and personalization.
• Collaborate with stakeholders to deliver data science solutions that facilitate business transformation.
• Lead individual use cases to ensure business needs are met and value is delivered.
• Apply mathematical rigor and innovative algorithm design to extract relevant insights from data.
• Translate unstructured business questions into quantitative problems and advise on appropriate analytical models.
• Prototype and iteratively optimize advanced analytics and machine learning solutions for business partners.
• Partner with IT to put machine learning models into production and evaluate algorithms for technical recommendations.
• Drive knowledge sharing in analytics skills across Digital Streams and relevant business units.
• Recommend and implement capability development paths to foster a data- and insight-driven culture.
• Monitor data science product performance and initiate corrective actions as needed.

Position Requirements

• Master's degree or equivalent in Computer Science, Econometrics, Mathematics, Operations Research, Statistics, Behavioral Science, or a related discipline preferred.
• Minimum 5 years of data science experience with a proven track record of delivering business-impacting solutions; proficiency in Python and Spark required.
• Experience with digital and multichannel marketing highly preferred.
• Advanced analytical skills with experience recommending algorithms and methodologies leveraging both logic and creativity.
• Experience with AWS cloud stack highly preferred; familiarity with visualization and wrangling tools such as Qliksense, PowerBI, and Alteryx an advantage.
• Ability to track latest analytics technology trends and manage multiple projects with a continuous improvement mindset.
• Sound communication skills to work with business partners of varied levels and backgrounds, articulating ideas both verbally and in writing.

Associate Data Scientist delivers high-quality machine learning and Natural Language Processing solutions in close collaboration with Data Engineering, Product owners, and business leaders to support organizational priorities and mission-critical capabilities. The work directly supports long-term data science strategy by owning algorithms, driving enhancements, and influencing senior leaders with strong business value propositions.

Operational Focus

• Work on data science projects in close collaboration with Data Engineering, Product owners, and business leaders to deliver high-value business capabilities.
• Be responsible for high-quality data science solutions with respect to accuracy, coverage, scalability, and stability.
• Maintain proper documentation and uphold code-reusability principles.
• Own algorithms and drive enhancements and optimizations per business requirements.
• Collaborate with Senior Data Scientists on long-term vision, strategy, and solution roadmap aligned with organizational priorities.
• Pitch ideas, present solutions, and influence senior leaders with strong business value propositions.

Knowledge Skills & Abilities

• Bachelor's degree in a quantitative field required; Master's preferred and PhD a strong plus.
• 1–3 years of hands-on experience building Machine Learning models, Natural Language applications, or AI business capabilities.
• Hands-on experience with Deep Learning frameworks including TensorFlow, Keras, and PyTorch; strong knowledge of Lean product principles, SDLC, and data science/ML topics.
• Proficiency in Python, PySpark, and key ML libraries and statistical packages; working experience with SQL/relational databases (Oracle), NoSQL databases (MongoDB, Neo4j), Linux, and shell scripting.
• Experience working within cloud computing environments such as Azure or AWS; demonstrated ability to translate quantitative analysis into actionable business strategies.
• Practical problem-solver with ability to collaborate across business, data science, and technical stakeholders.
• Competitive salary, generous PTO, charity match, Group Medical Insurance, Parental Leave, EAP, collaborative culture, and unlimited professional growth opportunities.

The Senior Associate Data Scientist produces analytical and machine learning modeling solutions for complex business problems as part of a cross-functional team of data scientists, business analysts, and software engineers. The Senior Associate Data Scientist leverages a broad technology stack including Python, Conda, AWS, and Spark to reveal insights from large volumes of numeric and textual data and translates that complexity into tangible business goals for stakeholders.

Key Deliverables

• Collaborate within a cross-functional team of data scientists, business analysts, and software engineers to deliver analytical and modeling solutions to complex business problems.
• Leverage a broad technology stack including Python, Conda, AWS, and Spark to reveal insights from large volumes of numeric and textual data.
• Build machine learning models through all phases from design through training, evaluation, validation, and implementation.
• Stay current on published state-of-the-art methods and technologies and apply them to emerging opportunities.
• Translate the complexity of data science work into tangible business goals for stakeholders.

Professional Experience

• Bachelor's degree plus 2 years of data analytics experience, or Master's degree, or PhD in a STEM field.
• At least 1 year of experience in open-source programming for large-scale data analysis and with relational databases; at least 1 year of experience with machine learning.
• At least 2 years of experience with machine learning and SQL.
• Experience in advanced statistical modeling and optimization including linear/logistic regression, tree-based modeling, generalized additive models, Bayesian statistics, time-series, non-parametric methods, optimization, and experimental design.
• At least 2 years of experience in Python, Scala, or R; experience working with AWS.

Embedded within the enterprise analytics function, the Associate Data Scientist develops enhanced analytic reporting models and data extraction workflows that support project planning, stakeholder recommendations, and organizational decision-making. Working closely with enterprise stakeholders and end users across E-Worker, Mobile, and Resident personas, the Associate Data Scientist advances data quality by recognizing accuracy issues, implementing resolutions, and providing technical guidance and training.

Areas of Ownership

• Play a critical role in project planning and execution, including creating roadmaps that account for timelines, dependencies, and key stakeholders.
• Recognize data accuracy issues, recommend potential solutions, and implement resolutions.
• Work collaboratively with enterprise stakeholders and make recommendations based on analytic insights.
• Lead the implementation and transformation of enhanced analytic reporting models.
• Assist in providing technical guidance and training to end users of data and applications.
• Frequently apply technical data extraction, manipulation, analysis, automation, and visualization using tools.

Education & Experience

• Bachelor's degree in Math, Business, Health Care, Data Analytics, or a related field with 2–5 years of experience; or Master's degree with 0–3 years of experience.
• Minimum 3 years of experience in data extraction, manipulation, analysis, automation, and/or visualization using tools such as SAS, SQL, R, Python, and Tableau.
• Fundamental understanding of business processes and metrics.
• Serves as primary contact on small projects.
• Demonstrates accountability, dependability, and sound analytical judgment; innovates and improves continuously.
• Strong communication skills with the ability to interpret business requests and build relationships with stakeholders.

Reporting to business and analytics leadership, the Associate Data Scientist II refines predictive models and machine learning algorithms that analyze customer data from company databases to identify trends, optimize product development, and drive marketing and business strategies. Partnering with stakeholders and end users, the Associate Data Scientist II strengthens decision-making by building monitoring tools, generating trend reports, and training teams on new dashboards.

Role Responsibilities

• Work with stakeholders to identify opportunities for leveraging customer data to generate business insights.
• Mine and analyze data from company databases to drive optimization of product development, marketing, and business strategies.
• Build predictive models and machine learning algorithms to analyze large volumes of data and identify client trends and patterns.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
• Create reports depicting trends and behaviors from analyzed data.
• Train end users on new reports and dashboards.

Background & Experience

• Experience with statistical and data mining techniques including GLM/Regression, Random Forest, Boosting, Trees, text mining, and social network analysis.
• Knowledge of customer domain and relevant sub-domain problem areas.
• Proficiency in Java, Python, and R; experience with web services including Redshift, S3, Spark, and DigitalOcean.
• Experience with analytics platforms such as Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, and Facebook Insights.
• Experience with computing tools including MapReduce, Hadoop, Hive, Spark, Gurobi, and MySQL; proficiency in SQL, NoSQL, SAS, Mahout, and MATLAB.
• Proficiency in data visualization tools such as Tableau or Qlik; proficiency in ETL, data processing, and spreadsheet tools such as Excel or Google Sheets.
• Proficiency in at least one version control tool such as Git or Bitbucket; experience with project management tools such as Jira.

Sitting at the intersection of data science and business strategy, the Associate Data Scientist Analyst applies machine learning techniques, time series, regression, and deep learning to large-scale predictive and descriptive analysis that acquires new customers and grows revenue. Operating across product development and existing lines of business, the Associate Data Scientist Analyst uses data visualizations and analytic requirements to drive business improvement and support key decision makers.

Job Functions

• Use data analysis and storytelling to make insights and recommendations that acquire new customers and grow revenue.
• Apply machine learning techniques, time series, regression, optimization, and deep learning to large-scale predictive and descriptive analysis.
• Drive new product development and improve existing lines of business through data analysis.
• Use data visualizations to clearly and effectively support key decision makers.
• Interact with business partners to understand goals, develop analytic requirements, and drive business improvement and optimization.

Minimum Qualifications

• Bachelor's degree in Mathematics, Statistics, Computer Science, Engineering, Data Science, or a related field, or equivalent education and experience.
• Familiarity with predictive analysis, cloud computing, machine learning, artificial intelligence, and Bayesian analysis.
• Proficiency in multiple statistical software packages and/or programming languages such as R, Python, SQL, Tableau, and Shiny.
• Advanced organizational and analytical skills with demonstrated ability to manage multiple tasks simultaneously.
• Excellent communication skills including the ability to identify and convey data-driven insights.

A key member of the data science team at Kin, the Associate Data Scientist advances customer experience and marketing performance by building predictive models, pipelines, and ML Ops infrastructure that support profitable growth. Collaborating with Data Engineering, Business Intelligence, and Analytics Engineering, the Associate Data Scientist monitors production model performance and acts as a strategic advisor to business partners using data-backed insights.

What You'll Do

• Building predictive models and perform analysis to optimize Kin's customer experience and marketing in order to continue profitable growth.
• Build pipelines to ingest data and create features to be used in models and analysis.
• Act as a strategic advisor to business partners by offering analytics, model support, and data-backed insights.
• Work cross-functionally with Data Engineering, Business Intelligence, and Analytics Engineering to deliver full product solutions.
• Assist with the design and architecture of ML Ops infrastructure and data vendor analysis and integration.
• Use analytics tools such as SQL and Looker to monitor the performance of production models.

Required Qualifications

• Bachelor's degree in Machine Learning, Statistics, Mathematics, Computer Science, or a related field.
• 1–2+ years of experience in Data Science, Business Analytics, Data Engineering, or Analytics Engineering.
• Insurance experience highly preferred; interest or experience in highly regulated and/or startup environments preferred.
• Mathematics or statistics background required; experience programming in Python and SQL in a professional setting required.
• Excellent interpersonal skills and passion for data-driven storytelling.

High-quality production software systems and terabyte-scale data pipeline performance depend on the Associate Data Scientist, who integrates research-driven machine learning models into production environments and builds internal tools for data science and analytics workflows. Based within a team that spans data engineering, software engineering, and product management, the Associate Data Scientist contributes to an equitable and inclusive learning environment while coordinating across customer-facing and technical stakeholders.

Day-to-Day Responsibilities

• Collaborate with data engineers and software engineers to integrate research-driven machine learning models into production software systems.
• Build internal tools for data science and analytics workflows.
• Manage data processing pipelines for analyzing terabyte-scale data from multiple sources.
• Write Python code following current best practices.
• Coordinate with product managers, software engineers, and customer-facing teams.

Qualifications & Experience

• Bachelor's degree in a quantitative field or equivalent experience.
• Experience with machine learning, statistics, and the scientific method; professional experience with software engineering, data warehousing, and Linux systems.
• Experience with AWS or other cloud-based server management and machine learning services.
• Programming skills in Python; experience using SQL for data cleansing, transformation, summarization, or analysis.
• Ability to communicate and collaborate with both technical and non-technical team members; conscientious, curious, dedicated, and quality-focused.
• Commitment to valuing diversity, practicing inclusive behaviors, and contributing to an equitable working and learning environment in support of EAB's DE&I Promise.