Top Senior Data Science Jobs

Build geospatial predictive risk models using LiDAR, GIS, terrain, asset, and weather data to simulate power-grid failures, vegetation risks, and infrastructure stress. Develop spatial data pipelines, establish QA/QC standards, translate complex metrics into investment insights, collaborate onsite with utility GIS teams, and mentor others in data science and analytics.

Leads the Enterprise Data and Analytics function, directing data science and engineering teams while advising senior government leaders. Oversees research methodologies, study design, program evaluations, statistical modeling, data governance, dashboards, and decision-support products. Establishes analytical standards, ensures compliance with government and healthcare data policies, coordinates with enterprise data organizations, performs advanced analyses, and supports staff development.

The Data Scientist will analyze complex healthcare datasets, build Python-based reporting pipelines, clean and validate data, and create tables, visualizations, and PowerPoint presentations. They will identify cost and utilization drivers, answer account questions, improve client-facing analytics, and collaborate with underwriting, medical management, sales, finance, and other business partners. The role requires translating complex analyses into actionable insights while managing multiple projects and deadlines.

Develops and refines machine learning, statistical, mathematical, and advanced analytical algorithms. Manages Big Data Platform activities, supports programming, data modeling, data management, visualization, analytics, workflow reproducibility, and data curation across one or more mission domains. The role requires an advanced quantitative or computer science degree, substantial relevant experience, and an active TS/SCI with polygraph clearance.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Own growth data science across acquisition, activation, experimentation, recommendations, pricing, monetization, and segmentation. Design rigorous experiments and quasi-experimental analyses, establish experimentation standards, develop metrics and datasets, and partner with Product, Engineering, and Data Engineering leaders. The role requires influencing product decisions, modeling freemium economics, evaluating personalization systems, and working independently in a fast-growing startup environment.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Lead a small data science team while remaining hands-on in building advertising audience, measurement, ranking, and data-feed models. Set technical direction, apply causal inference and experimental design, build scalable production systems, mentor scientists, and partner with product, engineering, sales, and business development teams. Translate campaign data into trusted measurement products, influence senior leaders and customers, and establish Fetch’s methodology as a market standard.

Own and deploy statistical, AI, and machine learning models for demand forecasting, inventory optimization, and Sales, Inventory, and Operations Planning. Analyze forecast accuracy, develop scenarios, manage data pipelines, create KPI reporting, and translate insights into operational recommendations. Partner cross-functionally with Sales, Marketing, Engineering, Purchasing, Production, Finance, and business units to support rolling 12–24-month planning and continuous process improvement.

Lead advanced analytics initiatives using predictive modeling, time-series analysis, clustering, churn reduction, anomaly detection, recommendation systems, causal inference, and quantitative research. Translate complex findings into executive visualizations, product metrics, and actionable strategy while partnering with Product, Engineering, and Business leaders.

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.

Own end-to-end product-led growth analytics, including self-serve funnel measurement, predictive modeling, experimentation, causal inference, data pipelines, and metric governance. Analyze activation, retention, monetization, churn, and usage drivers, then provide strategic recommendations to the CEO and leadership. Build and deploy production data models and machine-learning solutions while partnering across Product, Engineering, Growth, Marketing, and GTM.

Founding Data Scientist responsible for developing product analytics, earned media value metrics, attribution models, forecasting algorithms, and proprietary social commerce measurements. The role partners with engineering and product teams to improve data foundations, translate models into production code, and support public-facing data reports. Requires advanced Python, expert SQL, production software engineering skills, quantitative education, strong communication, and 4–7 years of data science experience.

Lead the end-to-end development, deployment, and maintenance of production-grade AI solutions. Design agentic AI systems with tool integrations, orchestration frameworks, and multi-step reasoning workflows. Develop LLM-based solutions using prompt engineering, evaluation, and monitoring practices. Build scalable automation applications with Python and AWS, while establishing and maintaining CI/CD pipelines for reliable deployment and continuous improvement.

Senior Data Scientist analyzing large-scale unstructured and semi-structured data using NLP, latent semantic indexing, exploratory analysis, machine learning, and clustering. The role researches novel approaches for distributed computing applications, visualizes complex results, and delivers recommendations for business decisions. Responsibilities include collaborating on data and intelligence platform improvements and using Python, R, SQL, Pandas, Jupyter, APIs, and visualization tools. The position requires extensive experience and an active high-level security clearance.

Leads fraud and risk-focused data science initiatives, using Python and advanced analytics to generate strategic insights and improve business performance. Manages client engagements, data pipelines, project execution, and teams while mentoring staff and resolving stakeholder issues. Responsibilities include building data architectures, developing visualizations, improving processes, and maintaining professional, technical, and independence standards.

Conduct product analytics for Super Cruise, autonomous driving, and ADAS using vehicle telemetry, fleet, safety, and customer data. Define trusted KPIs, build datasets, models, dashboards, and recurring analyses, investigate data-quality issues, apply statistical and causal-inference methods, and influence product roadmaps, launches, investments, and engineering priorities. Communicate findings, uncertainty, risks, and recommendations to technical teams, product leaders, and senior stakeholders.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Leads data science initiatives for US Card acquisitions, developing and deploying machine learning models across their full lifecycle. Partners with data scientists, software engineers, product managers, and business stakeholders to solve complex problems, analyze large-scale numeric and textual datasets, validate models, and translate technical findings into business outcomes. The role also involves team leadership, talent development, model governance, monitoring, and ongoing model improvements.

Leads data science initiatives for Capital One’s Card Mainstreet business. Partners with data scientists, software engineers, and product managers to develop customer-focused solutions; analyzes large-scale numeric and textual data; builds, validates, and implements machine learning models; translates technical findings into business outcomes; and supports innovation and team talent development.

Develop predictive models, algorithms, data pipelines, analyses, dashboards, and visualizations using large structured and unstructured datasets. Collaborate with clients and teammates to translate complex data into actionable insights and support informed decisions. The role requires expertise in Python, PySpark, R, SQL, Palantir Foundry, Power BI, statistical programming, and data science techniques, along with a Secret clearance.

Analyzes structured and unstructured data to produce actionable insights for national security decisions. Responsibilities include data cleaning, SQL and database analysis, time-series analysis, statistical and AI/ML modeling, Tableau visualization, technical reporting, briefings, research, ad-hoc analysis, and staff education. The role requires collaboration with customers and documentation of methods, findings, and applications using tools such as GitHub.

Develops machine learning and deep learning models for biologicals discovery in crop health. The role integrates genomic, metabolomic, and phenotypic data to prioritize microbial and natural product candidates, applies modern AI methods, and partners with scientists across biology, chemistry, genomics, and data engineering. Responsibilities include framing research problems, defining evaluation strategies, interpreting model outputs, generating biological hypotheses, and advancing predictive solutions through integrated discovery pipelines.