Top Data Science Jobs

Performs exploratory, descriptive, diagnostic, and predictive analysis supporting Navy Reserve readiness and mission assurance. Develops data models, dashboards, visualizations, and AI/ML solutions; analyzes data quality and anomalies; and supports automated monitoring and governance. Collaborates with engineers, architects, analysts, and government stakeholders to validate data, identify trends, gather requirements, and recommend solutions. Communicates findings through analytical products and decision-support tools.

Develop causal inference, experimentation, predictive, uplift, and treatment-effect models to measure business impact and improve targeting. Design and analyze A/B tests, work with large datasets, build scalable analytical frameworks and pipelines, and communicate actionable recommendations to technical and business stakeholders.

Develop and deploy data science and AI/ML solutions for scientific, engineering, and business problems within R&D. Responsibilities include data preparation, statistical modeling, machine learning, deep learning, computer vision, generative AI, LLM evaluation, predictive modeling, visualization, and pipeline development. The role collaborates with data engineers, scientists, domain experts, and stakeholders to scale AI solutions and communicate technical findings.

Build and validate analytical models using large structured datasets to improve operational and business outcomes. Translate complex findings into actionable recommendations, partner with engineering, design, and product teams to deploy production-ready solutions, lead high-impact federal projects, mentor junior staff, and shape data science strategy for Department of Education programs.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines using Python, SQL, machine learning, and large transactional datasets. Partner with Fraud Operations to refine models, reduce false positives and negatives, analyze emerging fraud trends, and improve underwriting and verification strategies. Communicate insights to senior stakeholders, apply AI in production, and lead team members and initiatives.

Develop and deploy machine learning, deep learning, and generative AI solutions for manufacturing analytics. Responsibilities include predictive modeling, process optimization, workflow automation, analytics platform development, stakeholder collaboration, model experimentation, and production system integration. The role also requires documenting solutions, applying MLOps practices, monitoring model performance, and supporting data-driven business initiatives.

Lead end-to-end development and deployment of predictive models, conduct advanced data-driven research, and solve ambiguous business problems. Partner with senior stakeholders and engineering leaders to shape strategic planning, resource allocation, and product innovation. Communicate complex findings to technical and executive audiences, architect scalable analytical solutions, mentor data scientists, and establish high standards for data science practices.

Develops advanced analytics and AI-driven solutions for CVS Health’s commercial product strategy. Translates healthcare and customer data into insights supporting product design, pricing, go-to-market execution, and performance optimization. Partners with Product, Sales, Actuarial, Marketing, and Underwriting leaders to identify high-value opportunities and deliver measurable financial outcomes. The role also involves stakeholder influence, experimentation, forecasting, product lifecycle support, and mentoring junior data scientists.

Leads technical work in climate data science, transforming atmospheric, oceanographic, environmental, earth observation, geospatial, and time-series datasets into actionable tools and insights. Partners with communities, policymakers, businesses, researchers, and cross-sector organizations; develops climate risk models and related analyses; translates complex science for nontechnical audiences; and leads collaborative projects, researchers, and data teams.

Leads data science, advanced analytics, dashboard development, data visualization, process automation, and metric governance for technology and enterprise operations risk. Develops descriptive, diagnostic, and predictive reporting; validates data quality, KRIs, KPIs, controls, and models; partners with stakeholders to deliver actionable insights. Uses SQL, Python, Power BI, Copilot tools, and Power Automate to improve reporting and operational efficiency while maintaining regulatory and internal control standards.

Leads advanced analytics, data visualization, dashboard development, reporting, metric governance, data quality, and process automation for technology and enterprise risk management. Uses SQL, Python, Power BI, Copilot, and Power Automate to create actionable insights, validate KRIs and KPIs, automate reporting, and improve operational efficiency. Partners with stakeholders, supports self-service BI adoption, maintains internal controls, and escalates risk-related issues. The role also requires dashboard standards, data modeling, visualization storytelling, and limited people leadership.

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

Lead data science across product and business areas, developing measurement frameworks, metrics, causal inference methods, forecasting models, and statistical analyses. Partner with product and business leaders on roadmap and growth decisions, apply AI and agentic workflows to accelerate analytics, communicate insights to senior stakeholders, and mentor other data scientists.

Develop product analytics and experimentation for Spotify’s Discovery Mode promotion tools. Build metrics, instrumentation, dashboards, and actionable insights to improve customer engagement and business outcomes. Collaborate with product, engineering, research, design, marketing, and other data science teams to identify opportunities, test hypotheses, and influence product decisions through clear data storytelling.

Design, develop, deploy, and monitor advanced deep learning models for fraud detection, risk management, and identity verification. Lead machine learning initiatives, research novel algorithms, manage end-to-end model lifecycles, collaborate with Product, Engineering, and Risk teams, and communicate insights to technical and executive stakeholders. Mentor data scientists and drive high-impact delivery, data quality, experimentation, and continuous improvement.

Develop and productionize statistical, econometric, and machine learning models using large, complex datasets. Lead data sourcing, ingestion, cleaning, drift monitoring, model refinement, and self-healing model frameworks. Create model outputs for business stakeholders, review code, support research approaches, and mentor less experienced team members. Ensure adherence to risk, regulatory, audit, and internal control standards.

Leads the design and production deployment of scalable data science and machine learning systems for aircraft, manufacturing, and operations data. Analyzes high-frequency sensor data, develops predictive models, builds Python tools and pipelines, establishes MLOps and engineering standards, and mentors data scientists. Partners with engineering teams and executives to translate complex analyses into strategic decisions related to aircraft safety, reliability, and performance.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Lead end-to-end AI/ML initiatives that improve student retention, engagement, enrollment, and re-engagement. Build and deploy predictive models, next-best-action logic, and production MLOps workflows using Databricks, MLflow, dbt, and Dagster. Own experimentation, monitoring, performance measurement, cross-functional implementation, vendor coordination, and executive communication. Mentor teammates and drive measurable outcomes across Product, Engineering, CX, and university partners.

Performs quantitative analysis, data integration, exploratory analysis, visualization, and machine learning to uncover insights from complex datasets supporting intelligence community missions. Uses Python and SQL to process data, develop predictive models, and communicate findings to technical and nontechnical stakeholders. Requires a TS/SCI clearance and bachelor's degree, with preferred experience in Spark, JEMA, web development, cloud services, ETL, geospatial data, and dashboards.

Leads the data science platform and machine learning strategy, building data marts, predictive models, MLOps infrastructure, and analytics capabilities. Analyzes healthcare claims, clinical, workflow, and operational data to risk-stratify patients, assess clinical program impact, support dashboards, and improve patient and provider engagement. Collaborates with clinical, operations, finance, product, executive, and technology teams while serving as a trusted analytics expert for a business workstream.

Develop predictive models and automated analytic solutions using structured and unstructured data. Responsibilities include collecting, cleaning, validating, and analyzing large datasets; building ETL pipelines; evaluating machine learning models; communicating findings to technical and non-technical stakeholders; mentoring users; and supporting mission-critical US PACOM analytics with an active Secret clearance.

Supports the analysis of complex data for National Reconnaissance Office mission efforts. Designs and integrates large datasets, evaluates data quality, prepares data for predictive modeling, and applies AI, machine learning, and statistical methods. The role also supports platform testing, quality assurance, process automation, technical documentation, data compliance, and ethical technology use while advising leadership on mission impact and data-informed decisions.

Build and operate AML data products, ETL pipelines, data models, integrations, and production data systems for financial crime prevention. Support machine-learning workflows, data quality, security, governance, observability, incident response, and operational resilience. Partner with AML experts on transaction monitoring, customer risk rating, sanctions screening, and surveillance initiatives. Improve engineering standards through reviews, mentoring, testing, secure coding, CI/CD, and automation.

Embeds with strategic customers to design, build, and implement AI-driven solutions that scale support operations and drive measurable Fin adoption. Analyzes customer workflows and data, establishes measurement best practices, prototypes production-aware tools, and partners with Sales, Success, Product, and Engineering. The role also feeds deployment insights into product strategy and requires strong communication, adaptability, statistical analysis, SQL, Python, and experience with AI/ML evaluation or LLM applications.