Top Data Science Jobs

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.

Conduct complex actuarial and predictive analyses supporting insurance product development, pricing, rating, reserving, financial analysis, and process improvements. Develop large-scale predictive models, multivariate analyses, SQL queries, tools, workflows, and business processes. Collaborate with actuaries, product managers, and business teams, communicate insights, improve efficiency, and mentor less experienced staff.

Develops data science, AI, machine learning, NLP, statistical, and visualization solutions for government audit and investigative work. Analyzes structured and unstructured data to identify fraud, waste, and abuse; builds and maintains models and analytical products; uses cloud data environments and relational databases; documents methodologies and technical requirements; communicates findings to stakeholders; and supports training, project collaboration, and agency mission objectives.

Lead the design and implementation of statistical and AI/ML solutions for product safety analytics. Develop and evaluate predictive, anomaly detection, classification, regression, NLP, and product similarity models. Apply semantic analysis, data visualization, and storytelling to generate actionable insights. Optimize scalable data science workflows in Azure using modern development tools, Git-based collaboration, and CI/CD practices while partnering with technical and business stakeholders.

Lead data science for a Grant product area, defining metrics, experiments, causal analyses, and decision-making models. Partner with product, engineering, marketing, and business leaders on strategy and roadmaps. Establish data science best practices across Kikoff and help shape AI-agent-enabled analytical workflows. Build production-quality SQL and Python pipelines, analyses, and models while guiding experimentation, monitoring, and policy decisions.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Develop advanced data science solutions for Intelligence Community missions, including scalable data pipelines, automation workflows, data models, visualizations, dashboards, and production-quality Python applications. Transform complex intelligence datasets into actionable insights, collaborate with analysts and mission stakeholders, manage multiple technical initiatives, and provide technical leadership. The role also supports geospatial and multi-INT analysis, modernization efforts, and potentially machine learning, cloud platforms, and DevSecOps practices.

Develop and deploy AI and machine learning models for production optimization, quality control, anomaly detection, predictive maintenance, supply chain forecasting, and operational analytics. Build scalable data pipelines, MLOps processes, APIs, dashboards, and LLM-powered solutions using prompt engineering, RAG, fine-tuning, and AI agents. Analyze complex datasets, conduct statistical experiments, monitor model performance, and promote responsible AI practices.

Analyze and clean data, develop statistical and predictive models, conduct A/B testing, identify business opportunities, and communicate insights to stakeholders. The role also involves evaluating data sources, creating tailored algorithms, implementing analytics, and monitoring model performance and data accuracy.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Develop and deploy applied generative AI and machine learning solutions using text, image, and audio data. Build, train, evaluate, validate, and operationalize NLP and deep learning models with LLMs, VLMs, and cloud technologies. Define AI observability and audit requirements, monitor non-deterministic system behavior, establish controls, curate training datasets, and collaborate with product, engineering, business, and data science teams to deliver scalable customer-facing solutions.

Design and maintain scalable data pipelines and analytical workflows using Databricks, Python, Spark, and SQL. Automate ingestion, transformation, reconciliation, and delivery across structured and semi-structured datasets. Implement data-quality controls, validation, schema enforcement, anomaly detection, and integrity checks. Troubleshoot ETL failures, improve processing efficiency, collaborate with mission stakeholders, and develop dashboards, models, and decision-support products for financial reporting and enterprise analytics.

Apply machine learning, artificial intelligence, statistical analysis, and causal inference to large datasets and product analytics. Develop insights and recommendations for recommendation systems and user experiences. Evaluate experiments through planning, monitoring, and post-analysis, build data pipelines and tools, and communicate complex findings to stakeholders to support business decisions.

Senior Data Scientist responsible for partnering with business leaders to frame ambiguous problems, deliver statistical and machine learning solutions, communicate insights, and drive measurable adoption. The role includes end-to-end analytical ownership, stakeholder influence, executive communication, Agile backlog management, cross-functional collaboration with engineering and technology teams, mentorship, governance, and improving analytics maturity.

Conduct data science and analytics consulting supporting DHS national security and law enforcement missions. Collect, clean, transform, and analyze large datasets; develop Power BI dashboards; identify patterns and anomalies; optimize processes through automation; and present actionable intelligence through reports and briefings. Requires active TS/SCI clearance, strong technical skills, and the ability to manage ad hoc analytical requests.

Build and maintain Marketing Mix Models to measure marketing effectiveness, develop attribution metrics, and create optimization solutions for budget allocation and scenario planning. The role applies statistical, Bayesian, time series, regression, and optimization methods using Python and SQL. It partners with marketing and cross-functional teams to translate analytical findings into actionable business recommendations and influence investment decisions.

Own the analytical strategy for a digital primary care program by defining KPIs, building dbt data models, maintaining the Snowflake/Fivetran/dbt stack, and delivering analytics, forecasting, revenue modeling, dashboards, and AI-assisted workflows. Translate patient, EHR, clinical, and operational data into actionable insights for product, care delivery, operations, leadership, health systems, and payers.

Leads data science research for a breast cancer program, developing multimodal clinical data frameworks, data dictionaries, machine learning pipelines, statistical models, and reproducible genomic analyses. Collaborates on computational tools, manages projects, drafts grants, creates training materials, supervises trainees, and supports complex data requests involving clinical, genomic, pathology, imaging, and administrative datasets.

Develop data collection tools, visualizations, dashboards, algorithms, and scripts to analyze structured and unstructured data. Build predictive analytics and machine learning solutions, integrate models into production applications, and create API-based data pipelines. Translate complex data into actionable insights for Air Force mission leaders while managing enterprise-scale data science projects and communicating findings to technical and non-technical audiences.

Build core dbt and Snowflake data models, analyze consumer payments, credit, pricing, and risk behavior, design experiments, create dashboards, improve data quality, and set technical direction while mentoring analysts and engineers.

The Data Scientist will conduct statistical analyses, develop reproducible code, manage data projects and pipelines, improve data governance, monitor data quality, maintain documentation and data dictionaries, and support transportation policy initiatives. The role also involves collaborating with technical and non-technical stakeholders, presenting findings, responding to analytical requests, improving databases and aggregate tables, and mentoring team members.

Analyze bike-share and shared-mobility data, develop predictive models, create Power BI dashboards, maintain databases and data quality, monitor vendor compliance, and provide research-based recommendations for planning, operations, policy, and future technology requirements.

Own the data science strategy and experimentation roadmap for ranking and relevance. Partner with product, engineering, ML, and commercial teams to improve marketplace outcomes through ranking models, experimentation, observational analysis, KPI frameworks, and commercial impact measurement. Balance advertiser, user, and platform value; guide launch and investment decisions; communicate findings to stakeholders; mentor scientists; and develop reusable analytical methods.

Develop and maintain predictive models for LTV, churn, retention, segmentation, personalization, and user acquisition decisions. Design experiments and causal analyses, apply LLMs to unstructured data, build AI-powered analytics tools, and productionize models with data engineering partners. Establish modeling standards, communicate insights to stakeholders, and mentor analysts and data scientists.