Top Data Science Jobs

The role involves innovating and implementing advanced data science models, collaborating with engineering teams, and translating complex data into insights to drive business decisions.

Lead end-to-end data science initiatives for commercial property or homeowners insurance: acquire and prepare data, build and validate predictive models, deploy and monitor production models, ensure governance and documentation, advise teams, and evaluate emerging ML/AI techniques to improve pricing, risk selection, and underwriting outcomes.

Lead data science projects across R&D, manufacturing, sales, and supply chain. Prepare and clean data, build regression and machine learning models (random forest, xgboost, lasso), perform cross-validation and hyperparameter tuning, query data with SQL, create plots and reports, communicate results to nontechnical stakeholders, identify high-value problems, and collaborate with data engineering and software teams to deploy models.

Lead methodology and modeling for causal inference, experimentation, and predictive analytics. Design and evaluate experiments, build production-ready models and evaluation pipelines, collaborate with Product/Economics/Research/Engineering, communicate findings to varied audiences, and document institutionalized analytical practices.

Lead measurement and optimization for performance marketing: design experiments, build and deploy statistical and ML models (LTV, propensity, forecasting), evolve incrementality testing, develop ETL pipelines and dashboards, translate findings into actionable recommendations, and collaborate cross-functionally to drive user growth and marketing strategy.

Lead data science for a new 0->1 B2B product: define strategy, frame high-impact questions, run experiments, design metrics/telemetry and dashboards, align global analytics, and translate insights into product decisions.

The Sr. Data Scientist will utilize statistical and machine learning techniques to solve healthcare-related problems and enhance predictive modeling across products, collaborating closely with various teams.

Lead product data scientist shaping growth strategy, experimentation, and metrics. Build self-service analytics, size opportunities, design and analyze experiments with product and engineering, drive activation/engagement/monetization insights, and evolve the company's experimentation culture.

Partner with trade leaders to translate ambiguous business problems into analytic frameworks, design and productionize Agentic AI solutions via APIs, perform statistical analysis, forecasting, experimentation, and synthesize large datasets into actionable insights to influence strategic and operational decisions across CVS.

The Senior Data Scientist will develop analytics solutions to transform healthcare data into actionable business insights, influencing product strategy and decision-making for CVS Health.

Senior Data Scientist on Core Analytics supporting Viewer Product to analyze structured and unstructured data, design A/B tests, build dashboards and data pipelines, generate actionable insights, advise product strategy for Home screen and discovery, and present data-driven recommendations to leadership.

Lead a data science team to build scalable analytics and predictive models for pricing decisions. Partner with business and engineering to analyze large datasets, prioritize work, ensure model quality, and present data-driven optimization recommendations.

Lead Revenue Analytics by developing data-driven strategies to maximize revenue, own and improve data pipelines, run advanced experiments and causal analyses, lead cross-functional initiatives, mentor junior analysts, and embed AI tools into workflows to scale impact.

The Senior Data Scientist will partner with multiple departments to deliver impactful data solutions, champion data adoption, and evolve data science standards.

The Lead Data Scientist will develop advanced analytical solutions for pricing and customer insights in pet insurance, mentor teams, and collaborate with stakeholders.

Drive data-science for people- and business-facing products: establish experimentation and instrumentation best practices, apply statistical and ML methods to web-scale data, build scalable analysis pipelines, and partner cross-functionally to turn insights into product improvements.

Lead analytics for user setup and onboarding: build dashboards, pipelines, alerts, and statistical models; run A/B tests and EDA; partner with product and engineering to drive adoption and provide data-driven recommendations to leadership to improve onboarding at scale.

Lead analytics for user onboarding: build dashboards and pipelines, run A/B tests and statistical models, analyze trends, create real-time alerts, and provide data-driven recommendations to product and engineering leadership to improve onboarding at scale.

Lead data science strategy across complex customer engagements, turn ambiguous business problems into repeatable ML solutions, define modeling and evaluation standards, drive data readiness and MLOps practices, mentor teams, produce reusable IP, and represent Microsoft in executive and thought-leadership forums.

Lead the development and implementation of account-level CECL models and frameworks while collaborating with cross-functional teams to ensure accurate credit loss estimation and management insights.

Proactively explore RevenueCat data to identify customer problems and opportunities. Translate ambiguous product questions into analyses and production-grade predictive/descriptive models. Partner with Product, Engineering, and Analytics to shape roadmaps, define experimentation and benchmarking approaches, deploy and iterate on models powering customer-facing features, and communicate insights across technical and non-technical audiences.

Founding Data Scientist for RC Capital responsible for building underwriting, fraud detection, anomaly detection, and lead-qualification models; integrating risk signals into product flows; deploying and monitoring production models; analyzing cross-platform revenue data; and defining DS strategy to scale fintech products.

Lead statistical scientist for R&D experiment design, meta-analysis, and predictive modeling in thin‑film solar fabrication. Integrate diverse data sources, design experiments, perform advanced statistical analyses (GLMs, PCA, regression, decision trees), conduct data forensics, and communicate results to program teams while training and mentoring staff.

Design and deploy predictive risk and segmentation models for commercial insurance underwriting and pricing. Apply actuarial principles, statistical and ML techniques, manage large datasets, validate and monitor models, and translate analytical outputs into actionable insights for product and underwriting teams.

Design and build scalable data pipelines and models to enable segmentation, attribution, real-time engagement, and analytics. Ensure data quality, governance, and GDPR/PII compliance while partnering with MarTech, analytics, and marketing teams to deliver production-ready datasets, event streams, and CDP functionality for customer journey orchestration.