Top Data Science Jobs in Richmond, VA

Lead and deliver productionized ML solutions across audience, marketing, pharma direct, and employer analytics. Design segmentation, propensity, attribution, and identity models; apply causal inference and experimentation; operationalize models with MLOps; evaluate generative AI/LLM use cases; set technical roadmap and mentor data scientists to drive measurable business impact.

Leads graph analytics, identity resolution, forecasting, and predictive modeling initiatives for operational, commercial, and product decisions. Designs scalable graph data models, production-ready machine learning solutions, and time-series forecasts. Establishes validation, monitoring, governance, and experimentation practices while partnering with product, engineering, and platform teams. Translates complex analytical findings into business recommendations and provides technical leadership across graph science, forecasting, optimization, and decision-support capabilities.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Lead the data science strategy for deployed humanoid robots: build predictive maintenance and reliability models, drive fleet performance and RaaS economics analysis, develop anomaly detection and RCA tooling, link manufacturing test data to field outcomes, and set instrumentation, experimentation, and data-quality standards across hardware, software, manufacturing, and operations.

Lead evaluation and data strategy for advanced AI, vision, and perception systems. Define metrics, build evaluation frameworks, audit artifacts, prioritize fixes, and translate model behavior into product decisions and data-improvement loops to ensure production readiness and downstream usefulness.

Design, build, and validate end-to-end ML models and AI/NLP solutions for pharmacy, claims, and clinical data. Responsibilities include feature engineering, explainability (SHAP), entity resolution, data quality, compliance-driven reporting, prototype front ends (Streamlit/BI), and collaboration with engineering, clinical, and client-success teams to productionize analytical outputs.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Own analysis and algorithmic solutions for ads delivery; train, deploy, and scale ML/optimization models into production; build logging, monitoring, and lineage; improve data pipelines; mentor data scientists; use agentic AI tools daily; collaborate with engineers to integrate models into ads systems and handle on-call production incidents.

This role involves analyzing EHR and insurance claims data to define clinical outcomes, developing AI models, and collaborating with technical experts for manuscript preparation.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

Design, develop, and evaluate conversational AI agents and generative AI products. Collaborate with AI scientists, ML engineers, and Responsible AI/Model Validation teams to ensure state-of-the-art, ethical solutions and support deployment and integration of conversational systems.

Lead analytical efforts in Real World Data research, develop specifications and methodologies, manage projects, and communicate findings to stakeholders.

Analyze large-scale structured and unstructured data, develop and validate data models and methodologies, and perform large-scale parallel processing in clustered computing environments (ECL/HPCC, Python, C, SQL) to produce mission-relevant analytic products supporting Intelligence Community objectives.

Develop and evaluate NLP and ML solutions on complex datasets, build text classification, topic-modeling and generative models, measure performance, run advanced statistical analyses, query data with SQL, use version control and GPUs, and present findings to leadership. Collaborate cross-functionally to deliver mission-critical analytics.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Oversee development, validation, monitoring, and governance of consumer credit risk models used for loss forecasting, credit decisioning, regulatory reporting, and risk management. Apply statistical, econometric, and machine learning methods to PD, LGD, and EAD models; support CECL, stress testing, and macroeconomic scenario analysis. Assess model performance, data quality, and compliance while communicating findings to senior leaders, auditors, regulators, and other stakeholders.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

The Associate Data Scientist partners with clinical and operational teams to prepare and transform data, develop predictive models, apply statistical and AI techniques, and create actionable insights. Responsibilities include feature engineering, model optimization, data visualization, dashboards, stakeholder presentations, risk management, and Agile collaboration to improve patient care, hospital operations, and organizational outcomes.

Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.

Build and evaluate machine learning and AI models (including computer vision and multi-modal approaches) to improve data quality and research workflows; run experiments, validate models, deploy demos/visualizations, and monitor and retrain models as needed while collaborating with product, engineering, and research teams.

Develop and refine predictive models, perform exploratory data analysis, and generate AI-driven insights for DoD missions. Integrate multi-source data, apply geospatial analytics, iterate models via Agile sprints, produce performance and cost reports, and optimize analytic workflows for operational deployment.

The Data Scientist will analyze data for risk and trading in Daily Fantasy Sports, develop predictive models, and monitor real-time data.

Develop and maintain reproducible bioinformatics pipelines and multi-omics data products. Perform statistical modeling, ML, and integration of genomic, transcriptomic, spatial, proteomic, and clinical datasets. Build researcher-facing dashboards, notebooks, and APIs; ensure FAIR, containerized, version-controlled analyses; collaborate with scientists, engineers, and government stakeholders to enable reusable, secure, analysis-ready data workflows.