Top Data Science Jobs in Washington, DC

Join PwC's Financial Crime Unit to analyze complex datasets using SQL and Python to detect AML/sanctions issues. Contribute to client engagements, explore ML, NLP and LLM approaches, build and deploy models, participate in research, and uphold professional and ethical standards while developing technical and commercial skills.

Apply machine learning and NLP to detect financial crime using SQL and Python. Analyze complex datasets, develop and monitor ML models, mentor junior staff, and translate client needs into analytical solutions while leveraging MLOps, cloud, and containerization.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

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.

Apply data exploration, cleaning, analysis, visualization, and predictive modeling to structured and unstructured data. Develop algorithms and ML/NLP solutions, work with clients to answer questions, and use distributed data tools to produce actionable insights.

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.

Develop and automate Python-based ETL and data pipelines in Databricks; clean and preprocess manpower/personnel data; build, train, and deploy machine learning models; create Qlik/Tableau reports and dashboards; maintain historical data and troubleshoot ETL; produce documentation and data products; collaborate with cross-functional teams to translate requirements into data-driven solutions.

Perform end-to-end data science work: exploratory analysis, feature engineering, training and validating ML models (including NLP and LLM-assisted tasks), writing data pipelines in Python/PySpark, and productionizing models in a Scala codebase while collaborating with product and engineering teams.

Design, validate, and deploy machine learning models to detect money laundering in large financial datasets; perform EDA, feature engineering, and model validation using Python/PySpark/SQL on AWS; build reproducible data pipelines and documentation; collaborate with compliance analysts and investigators and deliver clear visual reports.

Design and implement ML and predictive analytics solutions on multi-INT datasets. Retrieve, merge, and analyze structured and unstructured data, build AI/ML tools, and produce visual and written analytic products for defense customers. Support database administration, data engineering, and deliverables in a classified, mission-driven environment.

The Data Scientist will assess technical practices, perform data analysis, cleansing, and prepare agency performance metrics using various data tools and technologies.

Use data exploration, cleaning, modeling, and visualization to turn structured and unstructured IoT and other data into actionable insights. Develop predictive models, algorithms, and visualizations, work directly with clients, and apply ML/AI/NLP techniques to solve problems across public and private sectors.

Perform data exploration, cleaning, analysis, and visualization; develop predictive models and algorithms on structured and unstructured data; apply ML/AI/NLP techniques to deliver client-facing solutions. TS/SCI clearance required.

Perform data exploration, cleaning, analysis, and visualization; develop predictive models and algorithms using ML/AI/NLP techniques; collaborate with clients to translate data into actionable insights and deploy analytical solutions.

Develop data analytics and BI solutions for clients using SQL, Python, Power BI/Tableau; wrangle and visualize data; automate processes with Power Automate/Power Apps; build dashboards, work independently or in small teams, and present technical findings to technical and non-technical stakeholders. Support onsite client work in the Washington, D.C. area.

Build and validate ML and statistical models, including NLP/LLM and generative AI solutions; perform ETL and analysis using Python and SQL; create dashboards in Power BI/Tableau/Databricks; collaborate with data engineers and research new tools to solve federal client problems. US citizenship required.

Design and implement data pipelines and predictive models using SAP and non‑SAP data. Clean and validate large ERP datasets, perform statistical and exploratory analyses, build ML/AI solutions (SAP Analytics Cloud or external platforms), and produce dashboards and KPIs. Collaborate with IT and stakeholders to deploy data‑driven solutions and justify model value.