Top Senior Data Science Jobs in Washington, DC

Analyze complex client problems using statistical modeling, exploratory analysis, data visualization, and machine learning. Develop AI and GenAI solutions, data workflows, pipelines, models, and quality checks that generate actionable business insights. Communate recommendations through documentation and visual storytelling, manage workstreams, collaborate with clients, and mentor junior team members.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Designs and deploys advanced AI, machine learning, generative AI, and agentic solutions for fintech and payments. Responsibilities include owning the data science lifecycle, developing scalable ML pipelines, building RAG and foundation-model applications, applying AI to fraud, risk, forecasting, personalization, and payment optimization, and ensuring model governance, observability, privacy, and responsible AI practices. The role also mentors data scientists and partners with product, engineering, architecture, and business teams.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Develops data science solutions supporting Army and Department of Defense platforms. Responsibilities include API integration, automated data pipelines, Palantir Foundry and Army Vantage development, data architecture, executive visualizations, statistical modeling, predictive machine learning, neural networks, and LLM customization. The role translates complex datasets into actionable insights for senior leaders and requires collaboration across technical and military stakeholders.

Develop and deploy predictive models, machine learning solutions, and advanced analytics for classified government intelligence missions. Analyze complex datasets, build ServiceNow-integrated data solutions, and create Tableau dashboards. Partner with government customers and engineering teams, ensure classified data compliance, and mentor junior staff. The role is fully onsite in Washington, DC and requires an active TS/SCI clearance with polygraph.

Build and support secure, cloud-native platforms for data science teams. Responsibilities include architecting IL6/IL7 environments, enforcing IAM and least privilege, developing CI/CD pipelines, orchestrating Kubernetes workloads, implementing infrastructure as code and compliance automation, supporting reproducible Python environments, and enabling analytical tools in AWS. The role also involves platform modernization, observability, security documentation, collaboration with data scientists and security teams, and technical delivery leadership.

Analyzes usage data across 15+ web applications, builds data pipelines, develops predictive models, and introduces AI/ML tools to improve analytics. Creates executive dashboards and reports, identifies trends and anomalies, communicates actionable recommendations, and ensures data integrity, security, and compliance. Collaborates with technical teams and business stakeholders while documenting analytical methodologies and presenting findings to leadership.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing ETL pipelines, predictive models, machine learning algorithms, and automated analytic software. Collaborates with clients, guides teammates, and translates data into actionable insights. Requires Python or R, statistical and machine learning expertise, version control, TS/SCI clearance with polygraph eligibility, and a bachelor's degree. Preferred experience includes intelligence disciplines, deep learning, LLMs, DevOps, data visualization, and ontology development.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing predictive models, machine learning algorithms, ETL pipelines, automated analytic software, and data visualizations. Partners with clients to define needs, translates data into actionable insights, guides teammates, and supports solutions involving AI, IoT, and intelligence analysis. Requires extensive programming, statistical modeling, data engineering, and classified-environment experience.

Designs and deploys machine learning models, statistical analyses, data workflows, and custom algorithms for law enforcement, intelligence, investigative, and business datasets. Collects, cleans, processes, and analyzes large datasets; validates data quality and model performance; deploys predictive models; and presents actionable recommendations through visualizations to government stakeholders and executive leadership.

Develop proprietary foundation models, fine-tune domain-specific models, and architect multi-agent systems with autonomous reasoning, tool calling, and advanced RAG. Build curated and synthetic datasets for pretraining, alignment, and benchmarking. The role requires deep generative AI expertise, including LLM development, agent frameworks, foundation model architecture, and production-grade RAG pipelines, along with proficiency in modern model training, inference, vector infrastructure, and data technologies.

Lead data science initiatives involving entity and identity resolution, knowledge graphs, information extraction, search, predictive modeling, and AI-enabled discovery. Design data architectures and machine learning workflows, establish model development and monitoring standards, guide scalable data products, evaluate emerging technologies, and brief senior leadership on AI strategy, technical risks, and investment opportunities.

Conduct data science, engineering, and analytics work supporting Department of War and Intelligence Community clients. Test and evaluate AI/ML models, potentially work with large language models, perform all-source analysis, and collaborate with multidisciplinary teams. The role requires an active TS/SCI clearance with a recent CI polygraph and a bachelor’s degree or equivalent direct experience.

Analyzes structured and unstructured financial and ERP data using Python, SAS, statistical methods, predictive modeling, and advanced algorithms. Supports Oracle E-Business Suite financial processes, data conversion, reporting, and continuity of operations planning. Develops models and actionable insights for business decisions, collaborates with engineering and disaster recovery teams, maintains contingency plans, and executes recovery exercises. Requires a master’s degree, 5+ years of experience, Oracle expertise, certification eligibility, and approximately 25% annual travel to Washington, DC.

Build and validate analytical models using large structured datasets to improve operational and business outcomes. Translate complex findings into actionable recommendations, partner with engineering, design, and product teams to deploy production-ready solutions, lead high-impact federal projects, mentor junior staff, and shape data science strategy for Department of Education programs.

Builds dashboards, applications, automations, ETL integrations, predictive risk models, and AI/ML solutions for unstructured data. Performs data normalization, visualization, business intelligence analysis, and risk assessment improvement. Recommends data storage and integration solutions, creates Power BI documentation, and applies SQL, R, Python, and other data science tools to federal systems and public-sector projects.

Develop and maintain credit risk models for origination, behavioral risk, underwriting, and portfolio management across Latin America. Own the full modeling lifecycle, including feature engineering, validation, backtesting, monitoring, calibration, and implementation. Analyze large financial datasets using Python and SQL, monitor portfolio and model performance, address data quality and selection bias, and translate analytical findings into credit strategies. Collaborate with Risk, Data, Engineering, Finance, and Operations teams.

Develop and deploy complex machine learning and analytics models for client systems. Manage large datasets, perform data ingestion, cleaning, feature extraction, modeling, validation, visualization, and reporting. Collaborate with clients and distributed data science and systems engineering teams while delivering maintainable, tested, version-controlled, cloud-based solutions within cost and schedule constraints.

Lead data science, data engineering, and applied AI as the program’s senior technical authority supporting DIA analysts. Establish responsible and auditable AI standards, guide analytic tools from pilot through deployment in classified environments, mentor technical staff, develop training, coordinate with government and partner teams, support hiring, and remain hands-on in data science and tool development.

Provides data science consulting support to DHS by developing Power BI dashboards, cleaning and transforming data with Python and R, maintaining analytics solutions, and delivering predictive analytics and business intelligence. The role involves client communication, ad hoc analysis, requirements gathering, business development support, and translating technical topics for nontechnical audiences. Familiarity with cloud analytics platforms, PowerApps, ETL/ELT, financial data, and workload modeling is beneficial.

Conducts data analytics, engineering, mining, statistical analysis, predictive modeling, and intelligence-focused data visualization. Designs and optimizes data architectures, storage, access, integration, throughput, and query performance. Builds AI automation tools and prediction systems from disparate data sources, collaborates with intelligence community members, communicates complex findings, and develops concise defense intelligence products. Requires active TS/SCI clearance, U.S. citizenship, and ability to obtain a counterintelligence polygraph.

Develop and deploy machine learning models and statistical algorithms to detect financial crime using large-scale BSA/AML transaction data. Perform exploratory analysis, feature engineering, validation, SQL analysis, visualization, and documentation using Python, R, PySpark, AWS, and related tools. Collaborate with investigators, compliance analysts, and technical stakeholders to translate regulatory requirements into analytical solutions. The role requires active Top Secret clearance with SCI eligibility and full-time onsite work in Washington, DC.