Top Senior Data Science Jobs

Analyze complex operational, program, schedule, requirements, risk, and workstream data to create trusted metrics, dashboards, models, and decision-support products. Define KPIs and analytical logic, assess data quality, develop Python/R/SQL analyses, and support AI-enabled workflows. Partner with architects, engineers, leadership, and mission stakeholders to translate ambiguous operational questions into measurable insights and recommendations for FAA modernization and Digital Command Center initiatives.

Performs data science for a Joint Interagency Task Force supporting counter-UAS capabilities. Develops data taxonomies, analyzes data trends, provides data modeling for algorithms, and creates data-sharing mechanisms with external stakeholders. The role requires systems and cloud administration expertise, preferably with Microsoft Azure Government and Palantir Foundry, and involves onsite work in Alexandria, Virginia with occasional field travel.

Develops predictive models and transforms structured and unstructured data into actionable insights. Responsibilities include data wrangling, statistical analysis, machine learning, visualization, reporting, data pipeline management, cloud and DevSecOps integration, root-cause analysis, and technical solution development. The role supports government stakeholders, guides junior personnel, leads data projects independently, validates AI models, documents algorithms, and communicates findings through briefings and presentations.

Leads advanced data science and machine learning initiatives for healthcare payment integrity, including fraud, waste, and abuse detection, anomaly identification, predictive modeling, statistical analysis, dashboards, and recovery investigations. Provides technical leadership, mentors analysts, integrates production analytical solutions, collaborates with legal and business stakeholders, and presents findings to leadership. Requires healthcare expertise, advanced SQL and programming skills, and knowledge of claims, billing, reimbursement, and program integrity.

Develops standardized performance methodologies and analytical products using intelligence and operational data. The role identifies trends, anomalies, risks, and optimization opportunities; creates KPIs, scorecards, dashboards, reports, and executive briefings; and supports resource allocation, operational planning, collection management, and leadership decisions. Responsibilities include data validation, cleansing recommendations, documentation of analytical methods and limitations, and coordination with operational stakeholders, engineers, administrators, and leadership.

Develops and evaluates AI and machine-learning models for intelligence collection prioritization, tasking automation, resource optimization, and mission planning. Responsibilities include data preparation, predictive modeling, validation, monitoring, explainability, responsible AI, and collaboration with software engineers on deployment. The role briefs technical and mission stakeholders and requires work in a DIA SCIF supporting secure DoD and Intelligence Community operations.

Develop and deploy machine learning and AI solutions for healthcare affordability, copay intelligence, and patient access insights. Build predictive models, recommendation engines, scoring frameworks, and decision-support systems using claims, enrollment, and patient-level data. Operationalize scalable ML workflows with monitoring, governance, and retraining. Collaborate with engineering and business teams, communicate findings to leadership, mentor junior scientists, and ensure solutions meet quality, privacy, security, and compliance standards.

Own data science for payment performance, optimization, timing, retry strategies, monitoring, experimentation, and fraud detection. Build payment-health scorecards, anomaly monitoring, payment-risk models, and production ML systems using Python, SQL, PySpark, Databricks, and MLOps tooling. Partner with Engineering, Product, and Risk to shape payment strategy and roadmap while applying causal inference, feature engineering, and production model monitoring.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Leads operations research and data science initiatives across supply chain, procurement, and engineering. Develops optimization, forecasting, statistical, and machine learning models for inventory, scheduling, capacity planning, demand, cost, and vendor analytics. Establishes modeling, validation, backtesting, monitoring, and retraining standards; partners with business leaders to drive adoption; and mentors data scientists and AI engineers.

Own Yoodli’s data function and measurement strategy, including experimentation, LLM evaluation, analytics infrastructure, dashboards, and predictive models. Partner with product, engineering, executives, and customers to turn analysis into product and commercial decisions. Establish data standards, communicate recommendations, mentor teams, and potentially build the data science organization over time. The role requires hybrid work in the Greater Seattle Area three days per week.

Lead data science and marketing analytics initiatives that improve member activation, engagement, retention, and health outcomes. Develop segmentation, propensity, predictive, and prescriptive models; design experiments and A/B tests; establish measurement frameworks; and translate insights into campaign and product strategies. Partner with Product, Marketing, Care Management, Data Engineering, and MLOps teams to operationalize targeting models and scale personalized engagement programs.

Develop and deploy enterprise machine learning and data science solutions across the full lifecycle. Responsibilities include predictive modeling, statistical analysis, feature engineering, data exploration, model monitoring, MLOps, and cloud deployment. The role works with large-scale structured and unstructured datasets and collaborates with engineers, product managers, business stakeholders, and cross-functional Agile teams to deliver scalable analytical solutions and actionable business insights.

Develops NLP and machine learning solutions for VA disability claims, extracting and structuring medical and claims data. Builds and maintains NLP pipelines, tunes transformer models, validates accuracy, documents workflows, collaborates with VA stakeholders, and integrates models into government systems. Requires healthcare or claims data experience, cloud familiarity, and the ability to communicate technical concepts effectively.

Develop data science solutions for Navy mission applications by analyzing complex structured and unstructured data. Build algorithms, scripts, predictive analytics, statistical and simulation models, and machine learning solutions. Apply data engineering practices, statistical software, and natural language processing techniques to generate actionable insights that support military decision-making. Collaborate with customers to understand analytical needs and communicate findings.

Conduct quantitative analysis, process complex and geospatial data, develop visualizations, and create data-driven insights. Build Python-based data-cleaning and manipulation workflows while working with AI/ML technologies and multiple file formats. Desired work includes NLP document extraction, computer vision, automated collection and reporting, data standardization, and statistical techniques such as classification, regression, clustering, data reduction, and causal modeling.

Develops and deploys AI/ML and intelligent automation workflows for complex intelligence datasets. Builds statistical models, data pipelines, dashboards, and scalable software outputs within secure cloud infrastructure. Integrates data strategies with Zero Trust Architecture, supports reporting and technology prototyping, and collaborates with engineers, database administrators, and product owners. Requires active TS/SCI clearance and onsite support at Wright-Patterson AFB.

Architects and deploys enterprise-scale statistical, predictive, and machine learning solutions across healthcare, finance, operations, and member domains. Owns the full analytical lifecycle, including feature engineering, validation, monitoring, retraining, explainability, governance, and retirement. Partners with executives, engineering leaders, and platform owners to deliver production-ready analytics within Microsoft’s cloud ecosystem. Establishes standards for model risk management, auditability, reproducibility, and experimentation while guiding other data professionals and communicating insights to senior leadership.

Designs and maintains large-scale APIs and systems supporting money movement, translates product requirements into technical specifications, and guides product strategy. The role requires data science or quantitative modeling expertise, production model development, experimentation, SQL, Python or R, and translating analysis into business outcomes. Responsibilities also include reviewing API launches, ensuring system compatibility and extensibility, representing the team in technical discussions, mentoring engineers, and establishing engineering principles.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Leads supply chain data science initiatives involving time series forecasting, demand planning, buying, inventory optimization, replenishment, and machine learning. Develops and productionizes advanced forecasting and optimization models, manages ML pipelines and deployment, documents methodologies, troubleshoots code, and collaborates with cross-functional stakeholders. Mentors data scientists, supports Agile processes, and provides technical leadership across model development, monitoring, retraining, and continuous improvement.

Own the scientific and product roadmap for monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead cross-functional delivery with Product, Engineering, Sales, and GTM; define measurement standards, evaluate trade-offs, support customer pilots, influence commercial decisions, and turn bespoke studies into scalable products. Remain hands-on in SQL, Python, statistical modeling, and causal inference while mentoring scientists and establishing reusable methods.

Own audience or measurement products for Fetch’s monetization business, translating advertiser goals and purchase behavior into predictive models, datasets, and causal studies. Build and validate audience expansion, measure advertising outcomes, assess bias and uncertainty, and improve products using campaign results. Partner with Product, Engineering, Sales, and GTM, present findings to customers, and develop reproducible analyses and production-ready assets.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Build, evaluate, deploy, and monitor identity-resolution and record-matching models using large, noisy datasets. Design labeling and measurement strategies where no definitive answer key exists, use AI-assisted labeling, manage model retraining and lineage, review data quality, and communicate results and tradeoffs to product, engineering, and sales stakeholders. Mentor teammates and ensure responsible handling of personal data, opt-outs, retention, and lineage requirements.