Top Data Science Jobs

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Own projects from prototyping through production, conduct experiments, explore large-scale data, and collaborate with product, engineering, analysts, and account teams to improve attribution and marketing ROI.

Leads experimentation, causal inference, advanced statistical analysis, machine learning, and scalable data-product development. Partners with product, engineering, and research teams to generate insights, identify opportunities, influence executive decisions, and shape product strategy. Owns cross-functional projects, analytics roadmaps, reporting, stakeholder adoption, and methodological standards while translating complex data into actionable business outcomes.

Supports NGA’s Maven program by acquiring, curating, transforming, cleaning, analyzing, and visualizing complex geospatial and other datasets for AI/ML model development. Uses Python and quantitative methods to identify patterns, investigate inconsistencies, preserve data integrity, and communicate analytical findings, assumptions, limitations, and conclusions. The role requires active TS/SCI clearance and substantial relevant experience.

Supports NGA’s Maven program by acquiring, curating, transforming, cleaning, and analyzing complex geospatial datasets for AI/ML model development. Uses Python, JSON, XML, quantitative methods, and visualization to identify patterns, resolve inconsistencies, and produce reliable data foundations. The role also requires communicating technical findings, assumptions, limitations, and conclusions, with preferred experience in NLP, computer vision, data standardization, and advanced statistical modeling.

Develop machine learning models, statistical analyses, and automated data science processes to improve player acquisition, retention, monetization, and experience. Extract insights from structured and unstructured data, deploy and monitor production models, communicate findings to stakeholders, collaborate across data and business teams, mentor colleagues, and improve team processes.

Build production data science models from high-frequency aircraft telemetry, spatial data, and noisy ATC audio. Develop signals, features, weak-supervision labeling, evaluation methods, validation, and drift detection for unlabeled streaming data. Partner with engineers to deploy and operate models in production, while collaborating with aviation experts to translate operational knowledge into measurable intelligence.

Leads advanced data science modeling and fraud prevention strategies for consumer credit products. Analyzes complex datasets, designs and monitors fraud risk controls, balances customer experience with losses and operational costs, and communicates recommendations to technical and business stakeholders. The role also ensures data quality, drives analytical best practices, collaborates across risk, product, legal, compliance, and operations teams, and mentors junior data scientists.

Develop data-driven collections strategies and advanced models for Venmo BNPL and other PayPal products. Use SQL, Python, AI, experimentation, and analytics to create scalable portfolio measurement, A/B testing, and performance solutions. Partner with Credit Strategy, Fraud, Risk, Operations, Product, Technology, and Compliance teams; translate insights into business strategies; present recommendations to senior leaders; maintain data quality; and mentor junior data scientists.

Lead analytics for client engagements across data onboarding, study planning, predictive modeling, production deployment, and ongoing optimization. Develop models for audience targeting, segmentation, propensity scoring, and campaign optimization, primarily for commercial pharmaceutical applications. Partner with business, analytics, and IT stakeholders, lead technical workshops, present findings to senior audiences, and manage client relationships. Build machine learning pipelines using Python, SQL, and cloud technologies, while contributing client insights to internal product development.

Develop and deploy NLP, entity-extraction, and generative AI models for cyber datasets in a classified environment. Responsibilities include parsing logs, feature engineering, model evaluation, data-quality validation, Python workflow development, requirements translation, stakeholder demos, and collaboration with cyber experts and data engineers. The role is full-time and on-site at a secure government facility.

Develop revenue optimization and risk mitigation strategies using experimentation, causal inference, forecasting, Monte Carlo simulations, and machine learning. Build data pipelines, improve analytics infrastructure, generate insights for client-facing teams, and influence Product and Engineering roadmaps. The role requires strong strategic thinking, business impact orientation, and collaboration in a remote-first environment.

Lead complex data analysis and model development projects, building and deploying predictive models, machine learning algorithms, and AI solutions. Partner with engineering, product, finance, business, and analytics teams to translate objectives into actionable insights. Validate, monitor, and refine model performance while communicating technical findings clearly. Guide junior data scientists, improve team practices, and contribute to the data science roadmap.

Leads advanced data science modeling and provides independent second-line oversight of Buy Now Pay Later credit risk, underwriting, account management, risk controls, and exposure limits. The role analyzes portfolio performance, ensures data quality, challenges credit strategies, communicates insights to technical and non-technical stakeholders, and mentors junior data scientists while collaborating across geographically distributed teams.

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 enterprise-level data science initiatives, including pricing optimization, decisioning engines, statistical analysis, machine learning modeling, and business strategy development. Partners with executives, analysts, engineers, and business owners to translate complex problems into scalable analytical solutions. Provides technical and strategic leadership, mentors data science professionals, communicates insights to stakeholders, manages analytical risks, and supports regulatory and governance requirements across the bank.

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.

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.