Top Data Science Jobs

Support model risk governance by maintaining policies, documentation, inventories, validation plans, training, and stakeholder communications. Develop statistical programs and analytical solutions using R, Python, SQL, Hive, Spark, and cloud platforms to analyze large datasets and support credit risk and business intelligence consulting engagements.

Provides technical leadership across data science teams developing AI, NLP, machine learning, generative AI, LLM, RAG, retrieval, ranking, and recommendation solutions. Establishes architecture, engineering standards, evaluation and monitoring frameworks, governance, and compliance practices. Mentors teams, conducts technical reviews, guides high-risk implementations, and represents data science with senior stakeholders. The role requires strong Python and machine learning expertise, experience with text-rich datasets and search systems, and the ability to influence technical strategy across multiple teams.

Develop and evaluate applied AI capabilities for Elsevier’s LeapSpace research assistant. Responsibilities include building LLM-powered workflows, RAG and search pipelines, agentic systems, evaluation frameworks, experimentation methods, and semantic discovery tools. The role requires collaboration with engineering, product, UX, and scientific domain experts to productionize trustworthy AI solutions.

Own product and business analytics across experimentation, causal inference, instrumentation, KPI reporting, pricing, advertising, subscriptions, and conversion. Design experiments, analyze event-level consumer data, partner with engineers on telemetry, and communicate recommendations using statistical rigor. The role also supports thought leadership, manages an independent analytics roadmap, and informs product and business strategy.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Support Robinhood Gold growth by designing experiments, analyzing customer behavior, improving targeting and retention, and evaluating offers and campaigns. Partner with Product, Marketing, and Finance to guide product strategy, prioritize initiatives, and measure business impact. Use SQL, Python, modeling, and data visualization to generate actionable insights for the subscription business.

Develop and deploy advanced machine learning and data science solutions across large numeric and textual datasets. Partner with data scientists, engineers, and product managers to build customer-focused products. Lead model design, training, evaluation, validation, and implementation while researching emerging technologies and translating complex analyses into business outcomes.

As a Senior Data Scientist, the role involves leveraging data analysis, experimentation, and statistical methods to influence business decisions and enhance Gusto's product performance, while mentoring team members.

Develop fraud detection, risk assessment, anomaly detection, and risk-scoring models across factoring, payments, lending, and freight businesses. Analyze financial, behavioral, operational, and transaction data; identify fraud patterns; define risk signals and model metrics; communicate findings to stakeholders; and coordinate with Machine Learning teams to deploy models into production. Document methodologies and performance while partnering with Product, Credit, Risk, Operations, Data Services, and BI teams.

Own Berry Street’s growth measurement and investment decisions through experimentation, causal inference, attribution, media mix modeling, forecasting, and predictive modeling. Build production data models and automated ad optimization systems using messy marketing, product, and warehouse data. Guide channel budgets, creative, patient acquisition, LTV, and CAC while partnering with Growth, Product, Engineering, and leadership to drive durable cross-functional improvements.

Lead end-to-end data science and machine learning solutions, translating business problems into production-ready models, GenAI applications, and reusable technical patterns. Establish evaluation, governance, monitoring, documentation, and quality standards while mentoring data scientists and ML engineers. Collaborate with senior stakeholders, data engineering, platform teams, and distributed onshore/offshore groups to deliver maintainable solutions across statistical modeling, forecasting, NLP, optimization, and responsible AI.

Develop and deploy production-grade AI, machine learning, and generative AI solutions for insurance business challenges. Responsibilities include building LLM applications, prompt engineering, RAG workflows, data pipelines, experiments, proof-of-concepts, and scalable AI systems. The role involves monitoring model performance, generating business insights, collaborating with stakeholders, and communicating technical findings to diverse audiences.

Owns data and reporting methodology for transparency reports, risk assessments, audits, and regulatory compliance across Epic products. Translates legal and policy requirements into auditable metric definitions, maps moderation data to regulatory categories, and partners with Legal, Compliance, Trust and Safety, Engineering, and Data Engineering. Oversees reporting pipelines, validation tools, data quality, defect resolution, and recurring compliance reporting. Supports content moderation, child safety, audits, and regulatory information requests.

Owns data and reporting methodology for transparency reports, risk assessments, audits, and regulatory compliance. Translates regulatory requirements into auditable metric definitions, maps moderation data to regulatory categories, partners with engineering on data templates and validation tooling, reviews data quality, resolves defects, and presents methodologies to Legal, Compliance, Trust and Safety, and senior leadership.

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

Lead the design, development, deployment, and monitoring of machine learning and AI solutions for healthcare affordability and copay intelligence. Build predictive models, recommendation engines, scoring frameworks, and decision-support systems using healthcare, claims, enrollment, and patient data. Translate business challenges into scalable analytical products, communicate findings to stakeholders, ensure responsible AI practices, and mentor junior data scientists.

Develop recommendation, ranking, search relevance, and personalization models for digital shopping experiences. Apply modern machine learning and generative AI techniques, design offline and online evaluations, analyze experiments and customer cohorts, and partner with Product and Engineering on measurable outcomes. Build reusable personalization capabilities, move models toward production evaluation, mentor scientists, and improve technical and experimental practices across the team.

Own product experimentation, causal analysis, KPI frameworks, predictive modeling, and user segmentation to improve growth, engagement, retention, and revenue. Partner with Product, Engineering, and Design to translate ambiguous questions into rigorous analyses and launch recommendations. Develop lifecycle intervention intelligence, evaluate treatment effects and persistence, communicate uncertainty, and influence roadmap decisions through reusable analyses and clear narratives.

Own company-wide KPI, modeling, and measurement workstreams; develop trusted metrics, KPI trees, forecasts, causal analyses, scenarios, and diagnostic tools. Partner with Data Engineering, Product, Engineering, Finance, and Commercial teams to connect business drivers to retention, monetization, revenue, and margin. Build reproducible SQL and Python workflows, communicate uncertainty and assumptions, and drive adoption of analytical frameworks and recommendations.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes trusted metrics, partners with Finance, Product, Commercial, Engineering, and Data Engineering, and mentors scientists. The role drives adoption of governed self-service analytics and communicates assumptions, uncertainty, and business trade-offs to leadership.

Analyze federal workforce data and position descriptions using statistical modeling, NLP, machine learning, and text analytics. Develop dashboards, visualizations, and reports; map duties and skills to job series and grade levels; validate findings with HR professionals and leadership; and provide recommendations supporting accurate, compliant job classification and reclassification decisions.

Lead the design and production deployment of large-scale operations research and optimization solutions for pricing, inventory, supply chain, and marketing decisions. Build LP, MILP, and MINLP models; use optimization solvers; develop APIs, data pipelines, monitoring, and automation; evaluate solutions through scenario and sensitivity analysis; and communicate findings to stakeholders and leadership.

Develop and deploy enterprise machine learning solutions across the full data science lifecycle. Build predictive models, pipelines, and analytical applications; monitor model performance; and support data-driven decisions. Collaborate with stakeholders, engineers, developers, and product teams using cloud platforms, large-scale data, and modern MLOps practices. Responsibilities include statistical analysis, feature engineering, model evaluation, data exploration, deployment, optimization, and translating business needs into scalable recommendations.

Conduct healthcare data experiments, feasibility analyses, and evaluations of new data sources to improve audience-building capabilities. Translate client and business priorities into rigorous analyses and recommendations, collaborating independently with client success, product, engineering, and partnerships teams. Deliver client-facing results using healthcare claims, EHR, or similar data while supporting product requirements and commercialization outcomes.