Top Senior Data Science Jobs

Lead product analytics and experimentation for Fora’s travel marketplace. Analyze product usage and advisor behavior, define data instrumentation, guide roadmap decisions, build predictive and personalized models, and establish experimentation best practices. Partner closely with Product, Engineering, and Design while mentoring data team peers and improving processes, tools, and standards.

Lead marketing measurement and growth analytics through incrementality experiments, attribution, media mix modeling, customer value and retention modeling, and production machine learning. Partner with acquisition, lifecycle, membership, and growth leaders to guide investment and strategy. Mentor data teammates and establish best practices, processes, tools, and standards.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

Develop and maintain credit risk models for origination, behavioral risk, underwriting, and portfolio management across Latin America. Own the full modeling lifecycle, including feature engineering, validation, backtesting, monitoring, calibration, and implementation. Analyze large financial datasets using Python and SQL, monitor portfolio and model performance, address data quality and selection bias, and translate analytical findings into credit strategies. Collaborate with Risk, Data, Engineering, Finance, and Operations teams.

Analyze structured and unstructured data to guide Viewer Product strategy, improve Roku’s Home screen and discovery experiences, and identify growth opportunities. Build dashboards, reports, data pipelines, analytical models, and visualizations; design A/B tests; perform exploratory and advanced analyses; and communicate recommendations to product, engineering, marketing, and leadership stakeholders.

Develop and deploy data science, AI, and machine learning solutions for Unison business functions, including aerospace operations, fleet management, supply chain, and finance. Translate internal customer requirements into analytics tools and working solutions using complex datasets, LLMs, computer vision, neural networks, and advanced statistics. Collaborate with stakeholders, communicate technical findings, and support operational improvement initiatives.

Develop data science algorithms, analytical software, ETL pipelines, and machine learning solutions using Python or R and AWS. Analyze complex structured and unstructured datasets, integrate software tools, and deliver objective insights for client decision-making. The role requires statistical analysis, predictive modeling, version control, and a TS/SCI clearance with polygraph. Intelligence-community and DevOps experience are preferred.

Lead data science for growth monetization, including subscription conversion, churn, pricing, packaging, paywalls, offers, and revenue modeling. Design rigorous experiments, establish metrics and unit economics for new business lines, and serve as the data science lead for a product area. Partner cross-functionally with product, engineering, design, and data science teams to drive revenue growth and build scalable data foundations.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Leads agency-wide data science, reporting, data governance, and operations analytics. Responsibilities include developing performance indicators, assessing data quality, conducting statistical and geospatial analyses, supporting policy and research initiatives, coordinating data sharing, providing data to public stakeholders, using SQL to analyze large datasets, and contributing to public-facing transparency tools.

Build and deploy pricing, underwriting, and risk-scoring models for commercial property and general liability insurance. Use bind, quote, loss, and exposure data to identify profitable segments, predict carrier pricing, improve underwriting guidelines, and monitor model performance. Partner with program leadership to translate model outputs into actionable underwriting decisions and recalibrate models as the business grows.

Develop and deploy complex machine learning and analytics models for client systems. Manage large datasets, perform data ingestion, cleaning, feature extraction, modeling, validation, visualization, and reporting. Collaborate with clients and distributed data science and systems engineering teams while delivering maintainable, tested, version-controlled, cloud-based solutions within cost and schedule constraints.

The Senior Data Scientist develops and operationalizes predictive models and AI algorithms across investment platforms. Responsibilities include data analysis, feature engineering, model experimentation, deployment with MLflow and Docker, CI/CD automation, monitoring for drift and performance, data pipeline and lake design, governance, security, compliance, and stakeholder collaboration. The role requires translating technical findings into strategic recommendations and managing the full machine-learning lifecycle.

Leads research, statistical analysis, and predictive modeling across finance, risk, fraud, marketing, customer experience, and portfolio performance. Develops, deploys, monitors, and maintains advanced models; communicates findings and recommendations to leadership; mentors junior data scientists; and partners with business teams to apply analytics to strategic decisions.

Analyze product data and develop dashboards, pipelines, models, and A/B tests to improve Roku’s Home screen and discovery products. Provide actionable insights, identify trends and opportunities, guide product strategy, monitor key metrics, and influence leadership decisions. Collaborate with product, engineering, marketing, and other stakeholders to drive adoption, efficiency, and measurable business outcomes. Use AI and large language models to enhance analytical workflows.

Develop scalable operations research and data science solutions using optimization, simulation, machine learning, and information retrieval. Partner with technical and business teams to frame problems, build production-ready analytical models, evaluate performance, recommend technical roadmaps, and deliver actionable insights. Lead data science practices, communicate recommendations, improve model quality, and establish long-term processes, frameworks, tools, and standards.

Lead customer-facing data science for major advertisers, advising on campaign structure, measurement, bidding, and optimization. Translate advertiser insights into product roadmap priorities, define activation frameworks, design experiments, evaluate product features, and establish scalable global playbooks. Partner with product, engineering, sales, and go-to-market teams across regions while communicating complex analysis to technical and executive stakeholders.

Lead data science for a core fintech product area, defining metrics, experimentation strategies, causal analyses, and models. Partner with product, engineering, design, and marketing to guide roadmap decisions. Build AI product evaluations, automated scorers, regression tests, and model monitoring practices. Contribute to company-wide data science standards, AI-enabled workflows, and measurement frameworks while mentoring teammates and communicating recommendations to senior audiences.

Develops and applies data science, machine learning, predictive modeling, and big data techniques to analyze large datasets. Designs data-handling, search, storage, and processing approaches; develops software to improve data searches; validates data quality and processes; and delivers actionable recommendations. The role emphasizes scientific modeling and complex data challenges over visualization and traditional analytics.

Analyze complex structured and unstructured data; build and maintain data pipelines; develop predictive, prescriptive, NLP, clustering, forecasting, and statistical models; process data using cloud platforms and distributed frameworks; automate data workflows; create visualizations; collaborate with data engineers; and communicate findings to technical and nontechnical stakeholders supporting a U.S. Special Operations Command program.

Lead end-to-end data science initiatives, including exploratory analysis, feature engineering, model development, deployment, and monitoring. Develop scalable solutions for forecasting, fleet planning, maintenance, vehicle operations, and customer satisfaction. Partner with business teams, create leadership-ready data stories, develop MVPs, and mentor junior analysts. The role requires strong Python and SQL skills, machine learning experience, distributed data processing expertise, and the ability to translate complex analyses into practical business decisions.

Develop and migrate reporting assets from SAP BusinessObjects to QlikView and Qlik Sense. Analyze and rebuild Web Intelligence, Crystal Reports, and universes; debug legacy and new reports; optimize SQL, scripts, expressions, data models, and load processes; validate output accuracy; document migration logic; and collaborate with stakeholders on requirements and report specifications.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.

Lead data science, data engineering, and applied AI as the program’s senior technical authority supporting DIA analysts. Establish responsible and auditable AI standards, guide analytic tools from pilot through deployment in classified environments, mentor technical staff, develop training, coordinate with government and partner teams, support hiring, and remain hands-on in data science and tool development.

Conduct research and develop innovative data science and structured analytic solutions for Department of Defense intelligence challenges. Apply analytic methodologies, coding, data visualization, intelligence tools, and emerging technologies to operational data. Collaborate with analysts, engineers, and designers to create applied capabilities, document findings, and communicate results through written, verbal, and multimedia presentations. The role is on-site in Tampa and requires a Top Secret clearance with SCI eligibility.