Top Data Science Jobs

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Design and deliver analyses, reports, and dashboards; translate technical findings for business leaders; perform complex data queries and reconciliation; identify data capabilities and partner with enablement teams; mentor analysts and drive a data-driven culture across functions.

Analyzes large datasets using data science, machine learning, predictive modeling, and big data technologies. Designs approaches for data handling, search, retention, storage, and processing; develops software to improve data searches; validates data quality and processes; and reports actionable insights. The role emphasizes scientific analysis over visualization and requires an active Top Secret/SCI clearance with potential Counterintelligence Polygraph eligibility.

Develops AI and automation solutions for IT asset management, including stakeholder agents, anomaly detection, data quality improvements, predictive asset insights, data modeling, dashboards, and regulatory reporting. Leads resiliency review sessions, recommends actions within risk tolerances, translates business needs into data-rich outputs, and presents findings to diverse audiences. The role also supports infrastructure compliance, process improvement, project planning, and production support.

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior data scientists.

Conduct research and predictive modeling for Citi Bike and shared mobility programs; build Power BI dashboards, reports, and briefing materials; translate complex data into actionable recommendations; monitor vendor compliance and data quality; document data procedures; maintain internal databases; and collaborate with Operations, Planning, Policy, leadership, and other stakeholders.

Develop member-level predictive models and longitudinal analyses for CMS Health Outcomes Survey measures and Medicare Advantage Star Ratings. Analyze survey drivers, trajectories, sampling, response rates, case-mix adjustments, and statistical significance. Build data pipelines and dashboards, validate methodologies, deploy models with engineering teams, and translate findings into clinical and operational interventions. Collaborate with Stars, clinical, care management, compliance, legal, and survey vendor stakeholders to ensure CMS-aligned outcomes and reporting.

The Data Scientist will turn ambiguous B2B and go-to-market questions into actionable insights for strategy, monetization, product adoption, customer segmentation, field deployment, and organizational design. Responsibilities include analyzing product, customer, sales, revenue, and market data; partnering with cross-functional leaders; communicating recommendations to executives; applying AI tools; and helping establish the operating model for an emerging B2B data science team.

Build predictive failure models, real-time anomaly detection systems, diagnostic tools, and operational dashboards for spacecraft manufacturing, testing, and integration. Apply statistical modeling, causal inference, reliability analysis, and data mining to identify root causes, bottlenecks, quality issues, supplier risks, and process improvements. Develop reproducible Python and SQL analyses that support engineering decisions and mission reliability.

Build predictive models, anomaly detection systems, diagnostic tools, dashboards, and statistical quality-monitoring solutions for spacecraft manufacturing and testing. Analyze telemetry, production, supplier, and reliability data to identify failures, bottlenecks, delays, and root causes. Develop reproducible Python and SQL analyses and communicate actionable findings to engineering and operations stakeholders.

Build and maintain data science models and quantitative measurement frameworks to guide infrastructure decisions (chargers, parking, depots). Analyze infra efficiency and identify improvements. Collaborate cross-functionally with Supply Operations, Product, and Engineering to incorporate business context into model-driven decisions supporting expansion to new territories and cities.

Model CBRN and nuclear effects across operational environments, analyze hazard and toxicity data, automate large-scale modeling runs, and develop tools that transform raw modeling and observational data into tables, graphics, and actionable technical assessments. The role supports Air Force CWMD operations, technical research, operational evaluations, and advising operators on CBRN defense threats and test results.

Develops data science solutions by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Builds predictive models, algorithms, machine learning techniques, and quantitative analyses to answer client questions and support informed decisions. Works with clients and leads complex solution development. Requires Secret clearance, a bachelor's degree, and travel up to 25%.

Product Data Scientists will embed with iOS or web teams to define product metrics, analyze user behavior, design experiments, build statistical and machine learning models, and turn findings into actionable recommendations. They will create dashboards, experiment readouts, decision memos, and AI-powered analytics workflows while partnering with Product, Design, Engineering, and Data Engineering.

Leads data science and analytics initiatives for Department of Warfare clients. Responsibilities include collecting, validating, normalizing, and analyzing complex data; developing predictive models and machine learning solutions; building BI applications, dashboards, and reports; improving automation; documenting data quality and analytical findings; and presenting recommendations to stakeholders and government leaders. The role also provides technical thought leadership and supports research and development in AI and machine learning.

Develop data-driven decision analyses for Department of Warfare clients. Responsibilities include collecting, validating, and analyzing data; creating predictive models, trend analyses, dashboards, and business intelligence solutions; documenting data quality and findings; developing data frameworks and pipelines; automating processes; and communicating insights to government stakeholders. The role requires knowledge of DoW acquisition, financial, and PPBE processes, along with an active Secret clearance and five or more years of experience.

Develop algorithms and data systems to analyze structured and unstructured data, apply statistical and scientific methods, and generate objective insights for clients. The role requires programming in R or Python, data interpretation, and collaboration in a fast-paced environment. Preferred experience includes data cleansing, parsing, intelligence processes, development tools, and Windows and Linux environments. The position is onsite and requires TS/SCI clearance with a polygraph.

Leads data science initiatives involving modeling, simulation, decision analysis, predictive modeling, data exploration, visualization, machine learning, and NLP. Develops algorithms and quantitative solutions, guides teammates, collaborates with clients, and translates complex data into actionable insights. Requires extensive experience with programming, data analysis software, and complex solution development, plus a TS/SCI clearance with polygraph.

Leads UCI schema analysis, integration, compliance, and lifecycle management for Air Force mission-level Command and Control systems. Designs interoperable data flows, improves data quality, supports testing and validation, documents system changes, and collaborates with technical and operational teams. The role also applies data analytics, machine learning, and statistical methods within secure defense environments.

Build and implement AI and machine learning models, data pipelines, predictive analytics, attribution and personalization systems, and NLP tools. Develop Tableau dashboards, document models, communicate findings to technical and non-technical stakeholders, and collaborate with data scientists, engineers, and business teams on strategic initiatives. The role is fully remote within the United States and requires a quantitative master's degree.

Conduct research on how consumer LLMs search, reason, and synthesize information; design experiments and causal inference analyses; support product and engineering decisions with rigorous measurement; and build data pipelines and recurring evaluations that turn evolving research findings into product capabilities.

Performs data science for a Joint Interagency Task Force supporting counter-drone capabilities and national airspace security. Develops data taxonomies, analyzes data trends, provides data modeling for algorithms, and creates data-sharing mechanisms with external stakeholders. Requires systems and cloud administration experience, preferably with Microsoft Azure Government, and may involve travel to field locations. The role is onsite in Alexandria, Virginia and requires U.S. citizenship and an active Secret clearance.

Develop data science systems for motion imagery and full-motion video programs. Analyze aircraft operations and perform multi-object tracking on live video. Work with operational, archived, and live data in Elastic, Oracle, and Kafka; develop Python-based solutions and deploy code to Kubernetes using current GPU technologies. Subject matter expertise is welcomed, while the role supports mission-focused government capabilities.

Lead development of data science and ML solutions: explore, clean, analyze structured and unstructured data, build predictive models, create visualizations, guide teammates, and advise clients—often within secure DoD environments.

Develops data science, AI, and machine learning solutions using structured and unstructured data. Responsibilities include data exploration, cleaning, analysis, visualization, predictive modeling, NLP, algorithm development, systems integration, and client advising. The role guides teammates, develops automated workflows and data products, and applies tools such as Python, R, SQL, distributed computing frameworks, and visualization platforms to solve complex public- and private-sector challenges.