Top Data Science Jobs

Leads rigorous evaluation methodologies for autonomous vehicle performance using statistical analysis, machine learning, simulation, and on-road data. Develops metrics, detects drift and anomalies, identifies edge cases, correlates simulation with fleet data, and strengthens launch-readiness and safety decisions. Partners with engineering, safety, and operations teams, communicates findings to leadership, establishes developer self-service tools, and mentors colleagues.

Develop and implement data science, predictive modeling, machine learning, simulation, and data engineering solutions for laboratory automation and digital R&D products. Lead pilots from opportunity identification through adoption, collaborate with scientists and technology teams, improve laboratory data quality and usability, integrate models into workflows, and measure business impact. The role requires technical leadership across multidisciplinary teams and contributes to the long-term Digital Lab roadmap.

Lead marketing analytics and modeling to grow lending business: design experiments, build and validate campaign pipelines, measure funnel and ROI, manage direct-mail campaign selection and execution, create dashboards and provide data-driven recommendations to marketing and senior leadership.

Analyze business problems using SQL, Python or R, and statistical methods to identify trends, test hypotheses, and develop insights or recommendations. Visualize findings in Tableau and collaborate with data engineers to present results to stakeholders. The role requires clear communication, flexibility, and the ability to explain analytical approaches and adapt deliverables to business needs.

Develop predictive and statistical models, analytical datasets, data products, and AI-enabled workflows across marketing, ticketing, revenue, digital, and customer analytics. Analyze complex datasets, identify trends and data quality issues, validate AI-generated outputs, and translate business questions into scalable analytical solutions. Communicate technical findings and recommendations to technical and non-technical stakeholders.

Supports data science solutions for natural gas utility operations through exploratory analysis, feature engineering, statistical and machine learning model development, forecasting, optimization, data validation, visualization, and documentation. The intern will work with operational, weather, telemetry, billing, and usage data; contribute to Microsoft Fabric pipelines; complete an assigned project; and present findings to data teams and leadership.

Translate business objectives into actionable data analysis, analyze large datasets, apply statistical and machine learning methods, and clearly communicate findings to technical and non-technical audiences. The role requires Hadoop ecosystem expertise, Python scripting, strong statistical knowledge, sampling and analytical skills, and business domain experience in retail, enterprise mobility, or healthcare.

Use data science to source, ingest, clean, transform, analyze, and visualize structured and unstructured data in WDP. Develop dashboards and data products (Databricks, SQL, Python, Tableau/Qlik), create visual narratives, and brief senior DoW leadership to support IT portfolio management.

Leads applied AI/ML research as Principal Investigator and technical leader. Defines research portfolios, experiments, evaluation plans, and transition criteria; remains hands-on with prototypes, models, code, and end-to-end AI architectures. Leads cross-functional teams, mentors technical staff, evaluates emerging technologies, and transitions successful research into secure, scalable production capabilities using software engineering and MLOps practices. Communicates technical strategies to customers, business leaders, and engineering teams.

Develop predictive sales analytics products using machine learning, statistical modeling, optimization, and advanced analytics. Aggregate, normalize, and process large, inconsistent datasets to generate actionable insights. Apply supervised and unsupervised learning, boosting, ensemble methods, and empirical research. Collaborate with teams to create innovative products and bring them to market, using tools such as Python, R, SQL, Hadoop, cloud platforms, and visualization software.

Analyze large datasets using data science, machine learning, predictive modeling, and big data technologies. Design approaches for data handling, search, retention, storage, and processing; develop software to improve data searches; validate data quality and processes; and report actionable findings. The role emphasizes scientific data work over visualization and requires an active Top Secret/SCI-eligible clearance with potential CI polygraph eligibility.

Lead research, design, and evaluation of large-scale detection models for automated, fraudulent, and malicious activity. Define measurement frameworks for noisy or incomplete ground truth, mentor data science practitioners, advance AI/ML techniques, and shape long-term detection strategy. Partner with ML Engineering, Data Engineering, and Product teams to influence roadmaps and protect Cloudflare customers from evolving threats.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

Develop and deploy predictive and generative AI models for personalization, forecasting, marketing automation, conversational agents, and other business applications. Perform exploratory analysis, statistical modeling, causal inference, and A/B testing. Build Python-based modeling workflows, collaborate on productionization and MLOps, develop RAG and agentic systems, and communicate insights to technical and non-technical stakeholders across the organization.

Develops data analytics and machine learning solutions for vehicle diagnostics, prognostics, preventive maintenance, and intelligent controls. Processes high-volume engineering and service data using Hadoop, cloud systems, and modeling tools; identifies patterns, anomalies, and trends; supports validation, product improvement, research proposals, and analytics strategy. Collaborates with data engineering, software, engineering, suppliers, and business teams while guiding complex projects and technical roadmaps.

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.

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 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.

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.