Top Data Science Jobs in Washington, DC

Lead and architect enterprise-scale multi-INT data science solutions: build and deploy ML models, pipelines, and recommendation systems; advise customers on data constraints; review models and code for rigor; mentor and technically lead mid-level data scientists; evangelize reproducible best practices.

Support Defense Intelligence analysts by building data pipelines, ML models, and analytics for multi‑INT data. Perform data engineering, mining, predictive analysis, and deliver visualizations. Use Python, R, SQL, Power BI, and Tableau. Work requires active TS/SCI clearance and 8+ years of data science experience.

Senior full‑stack developer role to design, build, and lead web applications and backend services. Work with clients to define requirements, implement frontend and backend solutions, integrate databases and RESTful APIs, and support CI/CD, testing, and deployment. Mentor teammates and drive delivery across microservices and enterprise applications in a collaborative environment.

Implement and maintain geospatial data science and remote sensing workflows: acquire, clean, and process raster/vector and Earth-observation data; run and validate GeoAI and ML/DL models; conduct accuracy assessments and documentation; automate repeatable processing; collaborate with technical staff and translate results for partners and clients.

Lead design and oversight of end-to-end geospatial data science and remote sensing workflows; translate scientific questions into reproducible methods; validate and document analyses; mentor technical staff; guide model development, quality control, and delivery for environmental and conservation applications.

Develop and lead data science solutions: build ETL pipelines, clean and model structured/unstructured data, design algorithms and automated analytics, produce visualizations, use ML methods, collaborate with clients and mentor teammates to turn data into actionable insights for government and commercial problems.

Work with clients to formulate questions, explore and clean structured and unstructured data, build predictive models and algorithms, develop automated analytic software and ETL pipelines, and create visualizations and insights to support decision-making in intelligence and commercial contexts.

Lead development of advanced analytics and AI/ML solutions (including LLMs/agentic AI) for classified clients. Guide teammates, translate complex data into visual and narrative deliverables, evaluate model performance, and advise leadership on risks and decision criteria.

Build and lead full‑stack web applications: design and implement front-end and back-end services, RESTful APIs, and relational databases; maintain CI/CD pipelines, automated testing, and observability; work with clients, mentor teammates, and deliver Agile software solutions. Requires TS/SCI with polygraph.

Lead design, build, and deployment of production-grade generative AI products (LLM apps, agents, copilots, RAG/search). Rapidly prototype features, build APIs, services, and retrieval pipelines, optimize performance and reliability, ensure responsible AI, translate research into product, and mentor team members.

Build and deploy credit risk models for acquisition and account management. Create datasets and features from traditional and non-traditional sources, collaborate with credit, product, and engineering partners, run experiments, and monitor models in production to enable responsible lending decisions.

Work with clients to analyze structured and unstructured data, perform data cleaning and exploration, develop predictive models and NLP/text-mining solutions, visualize results, and mentor teams. Serve as a technical leader identifying data-driven opportunities and translating insights into actionable recommendations. Requires ability to obtain Secret clearance.

Mid-level data scientist who partners with clients (DoD/IC), analyzes large structured and unstructured datasets, evaluates and tests AI/ML models, mentors teammates, and delivers actionable insights to leadership to improve data science capability.

Perform advanced data science for Army NC4SI: develop ML models (ensembles, neural nets, LLM vectorization), design/optimize Army Vantage environments, integrate and deploy APIs and automated data pipelines across Army/DOD platforms, analyze complex datasets, ensure data architecture, security, and compliance in DOD cloud environments, and collaborate with military and technical teams.

Work with clients to translate data into actionable insights using machine learning, AI, and IoT data. Lead development of algorithms, automated analytic software, ETL pipelines, and data visualizations; integrate FOSS/COTS and support model deployment. Must hold TS/SCI clearance.

Design and implement data science solutions: ingest and clean data, build ETL pipelines, develop predictive models and algorithms, create visualizations, lead teammates, and deliver analytic products for intelligence and commercial clients while maintaining classified clearance requirements.

Lead data science engagements: work with clients to define problems, build ETL pipelines, develop machine learning models and algorithms, create visualizations, guide teammates, and deploy analytic solutions to inform decision-making across classified and unclassified environments.

Design and implement data science solutions: build ETL pipelines, clean and model structured/unstructured data, develop predictive and ML algorithms, create visualizations, lead teammates, and communicate insights to clients. Work often involves classified intelligence data and integration of FOSS/COTS tools.

Design and implement data science solutions: ingest and clean data, build ETL pipelines, develop predictive models and algorithms, create visualizations, and collaborate with clients to deliver actionable insights. Work often involves intelligence-domain data and requires active TS/SCI clearance.

Design, build, validate, and present production-ready NLP models: preprocess text, engineer features, select and train ML/deep learning/transformer models, evaluate results, and communicate insights to federal clients.

Lead development and deployment of machine learning models, provide technical leadership on AI/ML solutions, integrate models with data pipelines, build RPA automations, support data governance, evaluate analytics products, and present findings through dashboards and reports to stakeholders.

Perform claims-based HEOR and RWE analyses using Medicare and Medicaid data, design and execute analytic studies, produce Excel and PowerPoint deliverables, communicate results to stakeholders, manage data analysts, and assist with QA of programs and analyses to inform market access and policy decisions.

Design, develop, and deploy machine learning and NLP solutions at scale. Work with cross-functional teams to build content classification, representation learning, search, and recommendation models using classical ML, deep learning, and LLMs. Process large datasets with Python, SQL, and Spark, and communicate results to stakeholders with clear documentation.

Design, test, and implement supervised and unsupervised statistical and ML models to detect loan fraud and improper payments. Support criminal investigations with data analysis, develop repeatable data pipelines, document methods for evidentiary use, and build visualizations and dashboards. Collaborate with investigators, data engineers, and leadership; automate tasks with Python and Office/Power Platform tools; and identify new analytic opportunities.

Design, implement, and scale NLP and ML solutions to detect loan fraud and improper payments. Clean and analyze large unstructured corpora, build supervised and unsupervised models, support criminal investigations, document methodology to meet evidentiary standards, produce dashboards and reports, and coordinate with data engineering for production pipelines.