Top Senior Data Science Jobs

Analyzes pharmacy claims, dispensing, adjudication, and related healthcare data to develop clinical measures, dashboards, KPIs, medication cohorts, and actionable insights. Evaluates medication access, affordability, adherence, utilization, formulary performance, safety, and outcomes. Translates stakeholder questions into analytic specifications, validates measures, partners on evidence development, and supports governed reporting products while maintaining privacy and data governance standards.

Build and deploy applied data science models for auto insurance carrier matching, media buying, bidding, and ad serving. Own the full modeling lifecycle, including data discovery, validation, deployment, monitoring, and adaptation to late labels, sparse sessions, feedback loops, and market drift. Partner with business stakeholders and an ML engineer to scale production models and deliver measurable acquisition and ROAS outcomes.

Build and deploy AI and machine learning solutions for clinical development, evidence generation, trial design, and decision support. Apply statistical modeling, simulation, and experimental design to clinical, operational, and real-world data. Develop reproducible model pipelines, integrate algorithms into production applications, and support compliant, interpretable ML architectures. Collaborate with product, engineering, UX, and scientific teams while documenting modeling decisions and contributing to knowledge sharing.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Lead end-to-end yield optimization for Azure infrastructure by analyzing revenue, hardware, and capacity data. Develop statistical and machine-learning models for pricing, resource allocation, and demand elasticity; design experiments and causal inference analyses; build metrics and dashboards; and communicate actionable recommendations to technical, business, finance, and executive stakeholders. Mentor peers and guide cross-functional decisions involving cloud economics, pricing, customer behavior, utilization, and cost efficiency.

Leads people analytics research using causal inference, quasi-experimental methods, statistical modeling, and large workforce datasets. Partners with Recruiting, Compensation, Talent, Legal, and business leaders on hiring, retention, promotion, pay equity, quality-of-hire, and organizational effectiveness. Translates research into visualizations and actionable decisions, leads compensation analytics and governance reporting, and uses AI tools to accelerate analysis and synthesis.

Develops and maintains healthcare reports, dashboards, and data analyses using SQL, eClinicalworks, Power BI, FlexSim, Minitab, and Python. Translates EMR workflows into backend data structures, identifies trends and improvement opportunities, supports data quality and governance, builds custom reports, and coordinates with clinical, financial, quality, regulatory, operations, and external technology stakeholders.

Develop machine learning and statistical models across large numeric and textual datasets, from design through implementation. Collaborate with data scientists, software engineers, and product managers to deliver customer-focused products. Analyze data, validate and backtest models, apply techniques such as classification, clustering, sentiment analysis, time series, and deep learning, and translate technical findings into business goals.

Conduct large-scale data analysis, develop evaluation datasets, design experiments and A/B tests, define metrics, and assess AI and human evaluations for Microsoft 365 Copilot. Translate findings into product recommendations, dashboards, and executive-ready insights while partnering with engineering, product, and applied science teams. Mentor others and guide analytical best practices across complex AI product initiatives.

Support NGA’s Maven program by preparing imagery and other complex datasets for model development. Clean, transform, standardize, analyze, and visualize geospatial, JSON, XML, and other data using Python and quantitative methods. Investigate inconsistencies, preserve relevant information across transformations, evaluate assumptions, and communicate findings and limitations. The role also applies AI/ML knowledge, statistical techniques, and potentially NLP, computer vision, classification, regression, clustering, and causal modeling.

Support NGA’s Maven program by acquiring, curating, transforming, and analyzing imagery and complex datasets for AI/ML model development. Use Python, quantitative methods, visualization, and data standardization to clean data, investigate inconsistencies, preserve information across formats, and communicate findings. The role also involves interpreting geospatial, JSON, and XML data and may apply NLP, computer vision, classification, regression, clustering, and causal modeling.

Leads AbbVie’s omnichannel engagement analytics data science vision and technical roadmap. Develops predictive, causal, recommendation, next-best-action, generative AI, and experimentation solutions; establishes measurement and scientific rigor; and partners with product, engineering, commercial, regulatory, and technology stakeholders to productionize AI/ML systems. The role provides technical leadership, executive communication, mentoring, vendor oversight, and strategic guidance on AI investment and business value.

Develops advanced analytics, mathematical models, algorithms, simulations, and data-processing applications for Army mission data. Builds and evaluates machine learning solutions, supports MLOps frameworks, creates software tools, and enhances analytics applications. Requires database expertise, programming proficiency, modeling and simulation experience, machine learning deployment knowledge, and an active Secret clearance with U.S. citizenship.

Develops predictive and prescriptive models using statistical, machine-learning, deep-learning, and generative AI techniques. Leads end-to-end data science applications, studies, and proofs of concept; collaborates with business, technology, and statistical teams; mentors junior staff; creates KPIs and analytics; and ensures compliance with pharmaceutical regulations, GMP, and quality standards.

Conduct complex actuarial and predictive analyses supporting insurance product development, pricing, rating, reserving, financial analysis, and process improvements. Develop large-scale predictive models, multivariate analyses, SQL queries, tools, workflows, and business processes. Collaborate with actuaries, product managers, and business teams, communicate insights, improve efficiency, and mentor less experienced staff.

Develops data science, AI, machine learning, NLP, statistical, and visualization solutions for government audit and investigative work. Analyzes structured and unstructured data to identify fraud, waste, and abuse; builds and maintains models and analytical products; uses cloud data environments and relational databases; documents methodologies and technical requirements; communicates findings to stakeholders; and supports training, project collaboration, and agency mission objectives.

Lead the design and implementation of statistical and AI/ML solutions for product safety analytics. Develop and evaluate predictive, anomaly detection, classification, regression, NLP, and product similarity models. Apply semantic analysis, data visualization, and storytelling to generate actionable insights. Optimize scalable data science workflows in Azure using modern development tools, Git-based collaboration, and CI/CD practices while partnering with technical and business stakeholders.

Lead data science for a Grant product area, defining metrics, experiments, causal analyses, and decision-making models. Partner with product, engineering, marketing, and business leaders on strategy and roadmaps. Establish data science best practices across Kikoff and help shape AI-agent-enabled analytical workflows. Build production-quality SQL and Python pipelines, analyses, and models while guiding experimentation, monitoring, and policy decisions.

Develop advanced data science solutions for Intelligence Community missions, including scalable data pipelines, automation workflows, data models, visualizations, dashboards, and production-quality Python applications. Transform complex intelligence datasets into actionable insights, collaborate with analysts and mission stakeholders, manage multiple technical initiatives, and provide technical leadership. The role also supports geospatial and multi-INT analysis, modernization efforts, and potentially machine learning, cloud platforms, and DevSecOps practices.

Analyzes financial and ERP data using statistical analysis, data mining, predictive modeling, and advanced algorithms. Supports Oracle financial shared services, data conversion, business continuity, disaster recovery, and contingency planning. Develops analytical outputs that improve agency KPIs and identifies trends and business opportunities. Requires collaboration with systems, network, hosting, and disaster recovery teams, plus participation in COOP exercises and Oracle environment upgrades.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Develop and deploy applied generative AI and machine learning solutions using text, image, and audio data. Build, train, evaluate, validate, and operationalize NLP and deep learning models with LLMs, VLMs, and cloud technologies. Define AI observability and audit requirements, monitor non-deterministic system behavior, establish controls, curate training datasets, and collaborate with product, engineering, business, and data science teams to deliver scalable customer-facing solutions.

Senior Data Scientist responsible for partnering with business leaders to frame ambiguous problems, deliver statistical and machine learning solutions, communicate insights, and drive measurable adoption. The role includes end-to-end analytical ownership, stakeholder influence, executive communication, Agile backlog management, cross-functional collaboration with engineering and technology teams, mentorship, governance, and improving analytics maturity.

Senior Data Scientist partnering with Wealth Management and Private Banking to drive acquisition, retention, and AUM growth. Responsibilities include developing and deploying predictive models, preparing and analyzing large datasets, measuring cross-channel marketing campaigns, standardizing KPIs, and translating findings into actionable recommendations. The role requires collaboration with business, marketing, technology, risk, and compliance stakeholders, along with clear communication of insights to nontechnical audiences.

Build and maintain Marketing Mix Models to measure marketing effectiveness, develop attribution metrics, and create optimization solutions for budget allocation and scenario planning. The role applies statistical, Bayesian, time series, regression, and optimization methods using Python and SQL. It partners with marketing and cross-functional teams to translate analytical findings into actionable business recommendations and influence investment decisions.