Top Data Science Jobs

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Develop predictive models, customer segmentation, automated data pipelines, and analytics supporting CAHPS, HOS, and Medicare Advantage Stars strategy. Collaborate with engineering, clinical, compliance, coding, and legal teams to ensure CMS-aligned outputs. Analyze complex healthcare data, communicate insights to stakeholders, support dashboards and reporting, and help standardize AI-readiness workflows. The role requires advanced machine learning, programming, statistical analysis, and healthcare quality modeling expertise.

Analyze complex datasets and apply statistical, quantitative, qualitative, AI/ML, NLP, geospatial, and research methodologies to support national security missions. Develop automated workflows, models, dashboards, visualizations, and analytical tools; manage datasets and metadata; assess information environment and irregular warfare activities; and communicate actionable findings to operational leaders. Collaborate with intelligence, operations, research, and technology teams in classified environments.

Develops and supports data science and informatics solutions for DoD data, analytics, and AI initiatives. Responsibilities include building and testing machine learning models, analyzing large datasets, identifying patterns, creating data models, documenting requirements and findings, and presenting technical concepts to diverse audiences. The role requires experience with machine learning, programming, SQL, data visualization, terabyte-scale datasets, and GPUs, along with a Secret clearance and ability to obtain TS/SCI clearance.

Supports DHS clients through data science consulting, including dashboard and visualization development in Tableau, data cleaning with R and Python, analytics solution maintenance, reporting, predictive analytics, and ad hoc analysis. The role also involves client communication, business development support, and explaining technical concepts to nontechnical audiences.

Develop data science and AI/ML solutions for federal clients using Python, SQL, LLMs, Databricks, and cloud platforms. Responsibilities include data wrangling, model development and deployment, client requirement gathering, technical communication, solution design, and business development support. The role requires an active Secret clearance and collaboration with technical and non-technical stakeholders across remote, hybrid, or onsite Washington, D.C.-area projects.

Develop and maintain backend applications with a focus on NLP data preparation. Responsibilities include assessing and rewriting legacy Java applications, refactoring backend systems, building REST APIs, optimizing relational and non-relational database queries, and using testing frameworks to ensure application stability. The role requires extensive Java and Python experience, cloud and Linux expertise, and a TS/SCI clearance with polygraph.

Design and analyze rigorous messaging experiments using A/B testing and causal inference. Build measurement frameworks, data pipelines, dashboards, automated analyses, and scalable data solutions for messaging campaigns. Partner with CRM, Product, Algorithms, and Engineering teams to translate ambiguous business questions into actionable recommendations and experimentation standards. Support messaging strategy across channels, timing, content, targeting, and new business verticals.

Build custom reports and data products from client requirements through coding and ongoing delivery. Apply quantitative analysis, data mining, and research to generate strategic insights from raw datasets and algorithms. Partner with Engineering and Advisory Services teams to scale solutions and answer complex business questions. Independently manage projects while communicating with clients and stakeholders.

Perform data science, engineering, analytics, and statistical work for Department of War and Intelligence Community clients. Test and evaluate AI/ML models, collaborate with teams, communicate findings, and support or conduct all-source analysis. Experience with GEOINT and large language models is preferred. The role requires an active TS/SCI clearance and ability to pass a CI polygraph.

Conduct data science, engineering, and analytics work supporting Department of War and Intelligence Community clients. Test and evaluate AI/ML models, potentially work with large language models, perform all-source analysis, and collaborate with multidisciplinary teams. The role requires an active TS/SCI clearance with a recent CI polygraph and a bachelor’s degree or equivalent direct experience.

Develops and evaluates data science and AI/ML models while supporting Department of War and Intelligence Community clients. The role requires collaborating across teams, communicating effectively, conducting testing and evaluation, and potentially applying large language models and all-source analysis. Candidates must hold an active TS/SCI clearance with a current CI polygraph and have a bachelor’s degree or at least four years of direct data science experience.

Analyzes large-scale financial transaction data to identify patterns, anomalies, revenue opportunities, and risk-management insights. Supports THOR data product development, feature engineering, quality assurance, testing, documentation, production validation, and user support. Uses SQL, SAS, Teradata, statistical methods, and machine learning techniques while collaborating with technical and business stakeholders. The role also contributes to data visualization, presentations, training materials, and analytical solutions supporting regulatory compliance and operational efficiency.

Develop and deploy data science, machine learning, and AI solutions for risk, compliance, technology, and operational challenges. Analyze large datasets, build quantitative models and GenAI applications, document and test analytical solutions, and communicate findings to stakeholders. The role uses Python, SQL, LLM frameworks, Agile practices, CI/CD, and modern software engineering techniques while supporting model governance, risk management, automation, and enterprise analytics.

Supports AI and model development across Bank of America, applying data science, machine learning, statistics, and analytics to business and risk challenges. Responsibilities include developing scalable solutions, streamlining processes, documenting requirements, assessing AI governance, monitoring models, identifying risks, and communicating recommendations to technical and business stakeholders. The role also promotes AI technology reuse, compliance, automation, and measurable business value throughout the product lifecycle.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Drive data-informed decisions across Product, Sales, and Engineering by analyzing usage patterns, business performance, and trends. Build data pipelines, write SQL and Python analyses, create Looker and Data Studio dashboards, monitor opportunities and risks, and enable teams to use analytics tools effectively.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

Develop AI-driven planning, scheduling, resource allocation, and execution algorithms for autonomous spacecraft, vehicles, and mission operations. Apply search-based, constraint-based, satisfaction, optimization, geometric reasoning, and operations research techniques to complex mission data. Build software for automation, real-time decision-making, and scientific data analysis, while contributing to interdisciplinary teams and potentially leading development efforts.

Conduct machine learning and data science research for robotic exploration, science, and engineering applications. Develop algorithms, datasets, and models for airborne, orbital, and planetary mission data, lead small research teams, collaborate with scientific and engineering experts, publish findings, and support technology infusion across JPL and NASA. The role focuses on imaging spectroscopy, remote sensing, scientific validation, large-scale computing, and operational delivery of AI-driven technologies.

Develop and productionize AI and machine learning solutions for client, investment, and operational problems. Responsibilities include designing AI agents and agentic workflows, prototyping with foundation models and agent frameworks, building evaluation and observability systems, ensuring reliability and compliance, translating business needs into technical solutions, leading major initiatives, and presenting research and technical work to senior stakeholders.

Serves as the senior technical authority for data science and analytics supporting an Intelligence Community program. Leads teams, assigns work, and oversees AI/ML models, data pipelines, predictive analytics, NLP, anomaly detection, and multi-source intelligence data fusion. Establishes validation and quality standards, integrates emerging AI/ML technologies, and ensures analytical outputs are accurate, reproducible, and defensible.

Build and productionize machine learning, NLP, generative AI, and analytics solutions for healthcare and regulated federal data. Responsibilities include data preparation, feature development, experimentation, model evaluation, error analysis, validation, workflow monitoring, troubleshooting, and translating stakeholder requirements into technical solutions.

Analyze structured and unstructured data to generate actionable insights for clients. Develop algorithms, data workflows, visualizations, scalable data stores, and machine learning or AI solutions. Use R, Python, SQL, cloud platforms, and modern AI technologies while communicating findings to technical and nontechnical audiences. The role may support applications across fraud detection, cancer research, national intelligence, and other complex challenges.

Leads data engineering for the Sentinel program by developing, deploying, and maintaining production ETL/ELT pipelines from structured and unstructured sources. Oversees analytics data strategy, storage, governance, and quality; partners with stakeholders and visualization specialists; mentors junior engineers; conducts code reviews; and establishes engineering best practices for organization-wide analytics and decision-making.