Top Senior Data Science Jobs

The AI Data Scientist Sr designs and implements AI/ML solutions, collaborates with stakeholders, processes complex datasets, and optimizes business outcomes through data-driven analytics.

Lead data science efforts to integrate Discover and Capital One card models: build, validate, and implement machine learning models using big-data tools, partner with engineers and product managers, and deliver customer-focused model solutions.

The Director of Data Science role at Capital One involves leading a team to build data pipelines and machine learning models, leveraging technologies like Python and AWS to derive insights for improving customer financial decisions.

Senior Data Scientist responsible for building and deploying predictive models, performing EDA and statistical analysis, designing A/B tests, and creating scalable ETL/ELT pipelines. Work includes developing production-ready ML solutions, maintaining data quality in distributed environments (Spark/Hadoop), optimizing workflows, and collaborating with product, engineering, and business teams to translate requirements into reliable, monitored solutions.

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Lead development of open banking data science solutions (affordability/credit decisioning, identity/income verification). Apply end-to-end data science techniques, build/deploy dashboards, run customer trials, support deployment/MLOps, collaborate with stakeholders and Mastercard data science community to drive product excellence.

Lead data-science work for matching features: translate product goals into data problems, design and analyze A/B tests, perform exploratory analysis and data mining, generate strategic insights, and collaborate with AI, product, design, and engineering to develop and iterate production ML models.

Apply statistical analysis, predictive modeling, experimentation, and AI-assisted methods to support product analytics for small business P&C insurance. Partner cross-functionally to build lead/trigger capabilities, measurement frameworks, and AI-enabled use cases to improve client outcomes and product performance.

Perform advanced analytics and AI/ML to generate actionable insights for auto finance products. Build predictive models, measurement frameworks, and conversational AI solutions; partner with Product, Technology, and business leaders to drive growth, present findings, and refine KPIs and A/B experiment designs.

The Senior Data Scientist partners with Customer Success to perform deep-dive analyses, diagnose metric shifts, build predictive models (churn/upsell), apply causal inference and experimentation, measure business impact, and develop AI/LLM solutions. Responsibilities include self-serving across the data stack for extraction, pipelining, and dashboards, defining KPIs, and delivering actionable insights to reduce churn, drive revenue, and improve operational efficiency.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

Lead data science efforts to inform product and business strategy through quantitative analysis, experimentation, forecasting, and storytelling. Partner with cross-functional teams to define hypotheses, measure impact, influence roadmaps, present insights to senior leadership, and monitor key product metrics to drive value.

Lead web and marketing analytics: define tracking specs, implement and validate scalable tracking, join web and game data, build dashboards and reports, deliver campaign-level insights, measure feature effectiveness, and partner with stakeholders to make data actionable.

Serve as a senior analytics partner for consumer product experiences, owning experimentation, business analytics, and data to inform product roadmaps. Lead exploratory analyses, design multivariate tests, define success metrics, improve tracking and data quality, and build self-service AI-enabled reporting to surface insights about consumer behavior, content engagement, and media quality that drive product and business decisions.

Lead fraud risk strategy using advanced analytics: develop and implement models and metrics, analyze account lifecycle profitability, drive cross-functional initiatives, execute complex projects end-to-end, and present actionable insights to executive leadership to mitigate fraud.

Lead a team to design and execute analytics and test-and-learn programs for credit card products. Develop segmentation, campaign design, business case analytics, and MIS; present insights to senior stakeholders and evolve analytics tooling (Snowflake, AI-enabled apps).

Perform fraud analytics across the account lifecycle to identify risks and profitability drivers, develop monitoring and reporting, lead complex projects end-to-end, provide actionable recommendations, and communicate findings to stakeholders to guide fraud strategy and controls.

Lead credit-risk analytics for consumers and small businesses using large payment/customer data sets. Build attributes and models, deliver executive-ready insights, monitor emerging risks, prioritize high-impact projects, and identify GenAI opportunities to modernize analytics.

Lead data science efforts for a defense program onsite in Hazelwood, MO. Define data requirements, build analysis systems, design/deploy ML models, collaborate with engineers and SMEs, mentor junior scientists, ensure security/compliance, and communicate results to stakeholders while improving models and establishing best practices.

Lead people-analytics projects: analyze large HR datasets using statistical methods and ML (SAS/Python/R), build models and visualizations, deliver insights to stakeholders, and partner with data engineers and architects to operationalize solutions.

Develop and apply valuation analytics and statistical models in marketing, supporting decision-making and budget optimization through advanced statistical techniques and data analysis.

Lead analytic client engagements to design, develop, and implement predictive and prescriptive models. Partner with sales and business teams to identify opportunities, build data extraction and analysis pipelines (R, Python, SQL, Hive, Spark), present insights, and formalize repeatable processes. Serve as project owner from design through peer review, client presentation, implementation, and audit.