Top Senior Data Science Jobs

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.

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.

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.

The Senior Data Scientist will apply data manipulation principles to solve technical problems, support NATO force design, and strategic decision making through big data solutions and advanced technologies.

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.

The Principal Data Scientist - Oncology will lead efforts in standardizing and connecting biomedical and clinical data through the development of scalable knowledge graph infrastructure and AI-Ready datasets.

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 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.

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 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.

Lead applied research and engineering in data science and AI for cybersecurity: design experiments, build and deploy ML/LLM models, prototype solutions, co-author proposals, and present results to government sponsors and academic venues.

As a Senior Data Scientist at Databricks, you'll shape data science strategies, collaborate with stakeholders, mentor junior scientists, and build internal data products.

The Senior Data Scientist will design and deploy machine learning models, analyze complex datasets, develop analytical solutions, and mentor junior team members in a remote environment.