Top Data Science Jobs

Leads data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant acceptance, intelligence, and monitoring. Partners with data scientists, engineers, and product managers to deliver customer-focused products. Builds models through design, training, evaluation, validation, and implementation while translating technical findings into business outcomes and developing team talent.

Leads AI/ML and data science products that improve engineering productivity. Translates ambiguous business challenges into scalable, production-grade solutions, shapes scientific direction, ensures methodological quality, mentors scientists, develops reusable frameworks, manages risks and delivery, and collaborates across engineering, data, software, and business teams. Communicates technical tradeoffs to stakeholders and leaders while driving measurable enterprise impact.

Lead and execute complex analyses of large-scale health care claims, build data pipelines and analytic infrastructure, ensure data quality and reproducibility, manage projects and timelines, mentor junior staff, and translate results into policy-relevant insights.

Develops and deploys AI, machine learning, generative AI, and predictive analytics solutions for DHS clients. Responsibilities include building data and AI pipelines with Python, SQL, Spark, Databricks, and AWS; developing LLM, RAG, NLP, computer vision, and predictive models; evaluating model performance and responsible AI practices; creating dashboards and documentation; and supporting MLOps, DevSecOps, deployment, governance, and technical mentoring.

Leads advanced analytics and operations research initiatives using machine learning, predictive modeling, causal inference, optimization, simulation, and visualization. Develops and monitors models, translates business needs into scalable data strategies, delivers actionable insights, collaborates cross-functionally, and mentors data science team members. Supports supply chain network planning, capacity, inventory, transportation, fulfillment, and capital investment decisions.

Designs AI solutions for fund administration workflows, selecting models, prompts, retrieval, agents, rules, or human review. Builds evaluation datasets and metrics, runs offline and pilot evaluations, analyzes failure patterns, measures accuracy, efficiency, and cost savings, and packages successful approaches into reusable patterns.

Own the modeling and statistical lifecycle for enterprise AI initiatives. Build predictive models for anomaly detection, workflow forecasting, and risk mitigation; analyze and wrangle large-scale financial data; validate accuracy and bias; ensure regulatory compliance; create training datasets; establish model performance KPIs; and communicate quantitative findings to business and technology stakeholders.

Lead marketing data science for Venmo Paid Marketing by developing measurement frameworks, analyzing media effectiveness, conducting incrementality and geo-experiments, applying attribution and marketing mix modeling, and optimizing audience, channel, budget, and campaign investments. The role requires translating complex analyses into actionable recommendations, collaborating with cross-functional teams, ensuring data quality, managing analytical projects, and mentoring junior data scientists.

Leads a team of 10+ data scientists supporting underwriting transformation through forecasting, predictive modeling, anomaly detection, financial validation, reporting, and scenario optimization. Sets data science strategy, embeds analytical capabilities into products, defines metrics and OKRs, validates business outcomes, and delivers executive insights. Partners with Product, Engineering, Underwriting, Finance, Actuarial, and executive stakeholders while developing people leaders and managing organizational priorities.

Develops AML typologies and machine learning models to identify financial crime risks. Leads data analysis, ETL, model validation and deployment using Python, SQL, Azure and Databricks. Partners with Financial Intelligence Units and cross-functional teams, translates business problems into data science solutions, communicates insights to stakeholders, oversees complex projects, and provides technical leadership, governance and mentorship.

Provides technical leadership for FCRM analytics and reporting, including transaction monitoring, executive, governance, and regulatory reporting. Develops and maintains SQL, Databricks, and Python solutions using complex datasets. Owns deliverables from requirements through validation, documentation, and delivery; performs data reconciliation and quality control; translates ambiguous business questions into actionable insights; and communicates findings to senior stakeholders, regulators, and nontechnical audiences.

Conduct workforce analytics and applied research on attrition, engagement, mobility, performance, and planning. Perform statistical analyses, regression modeling, data extraction and validation using SQL and R or Python. Translate HR business questions into actionable insights, communicate findings to stakeholders, and protect sensitive workforce data.

Develop data products and analytical solutions supporting USSPACECOM. Responsibilities include building pipelines in Foundry and Databricks, applying SQL, Python, PySpark, and TypeScript, designing ontologies and data models, creating statistical and decision-support tools, validating data quality, developing dashboards and workflows, and briefing findings to technical and senior non-technical stakeholders.

Evaluate and improve AI mitigation systems, classifiers, and detection pipelines across high-risk domains. Build monitoring and measurement frameworks, analyze false positives and negatives, identify adversarial trends, quantify customer impact, and recommend prioritized interventions. The role requires rigorous experimentation, causal analysis, dashboarding, and cross-functional collaboration with engineering, product, research, and executive stakeholders to improve AI safety and preparedness.

Data Scientist Intern will analyze large-scale datasets, design A/B experiments, improve experimentation and analytics tooling, and translate insights into product recommendations. The role involves collaboration with Data Science, Product, and Engineering teams, use of SQL and Python, statistical analysis, AI-assisted workflows, and presenting findings to leadership. This hybrid internship runs June 1 through August 31, 2027, with three in-office days weekly in San Francisco.

Develop and maintain predictive Customer Lifetime Value models and eCommerce forecasts using transaction and behavioral data. Build statistical and machine learning models for revenue, retention, purchasing behavior, and customer demand. Perform cohort analysis, segmentation, scenario modeling, and advanced analytics while partnering with Marketing, Finance, Product, Data Engineering, and Analytics teams. Explore AI and LLM applications, communicate insights to stakeholders, and support scalable analytical workflows.

Build and maintain predictive Customer Lifetime Value models and eCommerce forecasts using transaction and behavioral data. Develop statistical and machine learning models for revenue, demand, retention, purchasing behavior, and marketing effectiveness. Conduct cohort analysis, segmentation, scenario modeling, and advanced analytics using Python, SQL, and cloud platforms. Partner with Marketing, Finance, Product, Engineering, and Analytics teams, communicate recommendations, and explore AI and LLM applications.

Conduct spatial and spatio-temporal statistical analysis of structured and unstructured intelligence data. Develop data strategies, mining methods, migration plans, database processes, and visualization capabilities. Manage data across NGA domains, ensure data quality and policy compliance, and support mission-specific analysis using ArcGIS, Tableau, R, and related tools. Collaborate with stakeholders and project future database administration requirements.

Develops data science, business intelligence, predictive risk models, dashboards, ETL processes, and AI/ML solutions for a federal client. Uses Power Platform, SharePoint, SQL, R, Python, and related tools to automate workflows, analyze large datasets, integrate enterprise data, assess cybersecurity and business risks, and deliver actionable insights and visualizations to organizational leaders.

Develop predictive models, algorithms, quantitative analyses, and visualizations from structured and unstructured data. Partner with the Marine Corps Directorate of Analysis and Performance Optimization to address data-driven questions, lead complex solution development, and translate disparate data into actionable insights. Apply programming, machine learning, natural language processing, and statistical techniques while guiding teammates and advising clients.

Leads end-to-end data science and optimization solutions for supply chain labor planning. Designs mathematical models, translates operational problems into scalable technical solutions, and builds across data, analytics, optimization, and application layers. Partners with Operations, Product, Data Science, and Engineering while owning deployment, production support, automation, reliability, and platform improvements. Applies Python, SQL, operations research, forecasting, simulation, and constraint optimization to improve labor planning and measurable business outcomes.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Analyzes structured and unstructured data to develop predictive models, algorithms, quantitative analyses, and visualizations. Applies statistical, machine-learning, artificial intelligence, and natural language processing techniques to help clients make informed decisions. The role requires Python-based data analysis, data exploration, cleaning, mining, and visualization, with opportunities to work on distributed computing, geospatial data, modeling, simulation, and defense or intelligence systems.

Develops predictive models, quantitative analyses, data visualizations, and machine learning solutions for Department of Defense clients. Responsibilities include data exploration, cleaning, mining, statistical programming, natural language processing, text mining, dashboard development, and advising stakeholders on actionable insights. Requires experience with Maven Smart System and a TS/SCI clearance.