Top Data Science Jobs

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.

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.

Transform analytical models into interactive GEOINT visualizations and analyst-facing interfaces, streamline production workflows, identify trends and insights, and operationalize geospatial data using Python, SQL, GIS tools, version control, and dashboarding frameworks to support mission-critical NGA/USSOCOM operations.

Lead experimentation in user migration and marketing strategies, applying advanced statistical techniques and causal inference methods to drive business decisions.

The Senior Data Scientist will oversee experiment design and analysis for user migration initiatives, collaborating with cross-functional teams and utilizing advanced statistical methods for decision-making.

Deliver end-to-end data science projects using machine learning and deep learning, collaborate with stakeholders, and support client engagement through analytical planning and solution design.

Design, build, and productionize optimization, forecasting, and simulation models to support global supply chain decisions. Develop end-to-end pipelines (ingest, feature engineering, training, validation, deployment), perform scenario and sensitivity analysis, and collaborate cross-functionally to translate business needs into scalable analytic solutions and reusable components. Support model governance, monitoring, and adoption through storytelling and stakeholder engagement.

Own complex analyses and measurement frameworks for a product or business area. Design experiments and statistical/causal models, define and monitor KPIs, quantify impact, and translate findings into product and business recommendations. Partner cross-functionally, mentor junior analysts, and use tools like Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to scale analytics and ensure reproducibility and strong governance.

Lead growth-focused data science to accelerate user adoption, engagement, and retention. Define measurement frameworks, build funnels and reusable datasets, design and analyze experiments using causal inference, develop behavioral segments, and partner cross-functionally to translate insights into product decisions and roadmap priorities.

Lead growth-focused data science work to accelerate adoption, engagement, and retention. Define measurement frameworks and core metrics, build funnels and reusable datasets, design and analyze experiments, apply causal inference, create behavioral segments, inform roadmaps, and partner cross-functionally to turn insights into product bets and measurable outcomes.

Lead end-to-end data science for aviation supply chain and operational data: integrate disparate sources, perform quality assessments, build predictive and statistical models (classification, regression, time series), document algorithms, and communicate results to stakeholders.

Lead a data science team to design, build, and deploy large-scale, real-time AI/ML solutions (forecasting, diagnostics, LLM chatbot, retention models, optimization) to optimize GPU cloud gaming performance and user experience.

The Senior Data Scientist will develop and maintain ML models and analytical frameworks to enhance customer experiences at CarMax, collaborating with Product and Operations teams to drive business-critical decisions and improve operational effectiveness.

Lead data science initiatives for pricing and demand forecasting using deep learning and time series methods. Design and productionalize models, communicate insights to business leaders, mentor junior data scientists, manage project priorities, and collaborate cross-functionally to drive profitable pricing decisions and scalable analytics solutions.

Lead development and operationalization of data-driven financial forecasting (time-series, AI/ML, causal, Monte Carlo). Integrate statistical outputs into Anaplan planning, establish model governance/validation, build templates/dashboards, collaborate with FP&A and Data/IT, and create a Center of Excellence for advanced forecasting and repeatable playbooks.

Develop, validate, and deploy predictive and statistical models on large tax and financial datasets to detect fraud and non-compliance. Perform data exploration, feature engineering, and model evaluation using Python/R, SQL, and Databricks. Collaborate with government clients and cross-functional teams, deliver technical and non-technical briefings, and ensure models are explainable, defensible, and maintainable in secure government environments.

Lead data science efforts to design experiments, build statistical and ML models (audience segmentation, bidding, LTV, attribution), develop time-series and real-time event understanding, productionize models with engineering, and communicate insights to inform product and commercial strategy.

Lead product and program analytics for Microsoft skilling experiences: design learner journey/funnel analyses, define success metrics, run experiments and causal analyses, build proficiency/competency models, shape telemetry and scalable reporting, recommend content and curriculum improvements, and mentor other data scientists to drive product decisions and learning outcomes.

Own analytics for institutional counterparties, build account-level metrics (volume, flow composition, spread capture, fee/rebate economics), monitor counterparty health and concentration risk, quantify institutional impact on market quality, support negotiations with data-driven valuations, and operationalize recurring reporting with analytics engineers.

Own full-funnel growth analytics for a vertical (paid acquisition through repeat engagement). Advise marketing on spend efficiency and channel mix, size/prioritize opportunities, design and analyze experiments, build cohort/funnel/retention analyses, productionize recurring analyses with analytics engineers, and document end-to-end work for team use.

Build and own the finance reporting layer in the data warehouse, reconcile ledger and platform transactions, produce production-ready financial reports, analyze fee and promotion impacts on revenue and retention, support monthly close and forecasts, and document models and reports end-to-end.

Lead end-to-end analytical workstreams to measure and improve acquisition, activation, retention, and advice. Design and analyze A/B tests, build and productionize churn/LTV/propensity and causal models, define data foundations with analytics engineering, create durable analyses and dashboards, and influence product, growth, and marketing decisions.

Lead analytical projects end-to-end to inform product, marketing, and commercial strategy. Build segmentation and predictive models (churn, LTV, propensity), design and analyze experiments, apply causal inference, and build AI/agentic workflows to operationalize decisions. Communicate findings to leadership and partners, collaborate cross-functionally, and contribute to team practices through code reviews and knowledge sharing.

As a Senior Data Scientist, you will enhance GEOINT production by developing data visualizations, utilizing Python and SQL, and supporting operational analytics for mission success.

Lead end-to-end data science projects to build, validate, and deploy statistical and machine learning models. Drive experimentation, causal analysis, and decision-support analytics for product and business stakeholders, mentor junior team members, and partner with engineering to productionize models and measurement frameworks.