Top Data Science Jobs in Richmond, VA

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Build, optimize, and deploy machine learning models (especially time series) using Python, PySpark, and cloud tools. Perform feature engineering, evaluate model performance (MAPE, RMSE, R²), and create ETL/data pipelines with integration tools. Work with Palantir Foundry, Google Cloud (BigQuery, Vertex AI), AutoAI, and data warehouses to support large-scale analytics. Collaborate with stakeholders, monitor production models, and continuously improve model accuracy and pipelines.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

Build and deploy AI/ML solutions from prototype through production for CMS, including generative AI, LLMs, RAG, intelligent classification, knowledge management, agentic frameworks, and enterprise integrations. Evaluate AI coding tools, establish AI/ML roadmaps, scale adoption, mentor data scientists, and translate technical concepts for stakeholders and leadership.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Designs, validates, and operationalizes predictive, machine learning, and AI models using large structured and unstructured datasets. Performs statistical analysis, develops visualizations and dashboards, presents findings to stakeholders, and integrates solutions across teams. Provides clinical-program analytics, evaluates data science tools, mentors junior scientists, leads concurrent projects, and prepares end-to-end model pipelines for business use.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Lead research projects that develop and improve data science methodologies and algorithms. Analyze existing solutions, build and test data extraction and transformation architectures, and collaborate with Technology and Product Management on deployment. Investigate production and client data issues, provide data analysis support, maintain SQL, Python, and R code, document processes, and present research findings to internal clients.

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations; and drive measurable outcomes with cross-functional stakeholders. The role requires independent analytical work, strong problem-solving, persuasive communication, and ownership of ambiguous initiatives.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Own end-to-end analytical functions for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; derive insights; communicate recommendations; and drive decisions and measurable outcomes with cross-functional stakeholders. The role requires independent data work, first-principles problem solving, persuasive communication, and ownership of ambiguous initiatives.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Develop machine learning and AI models using clinical, claims, and social determinants of health data to improve pediatric patient outcomes. Partner with stakeholders and cross-functional teams to define requirements, optimize data collection, build data assets, conduct exploratory analysis, and deliver strategic and patient-care insights. Query Snowflake independently and present findings to nontechnical staff.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

Analyze and optimize digital partnership performance, growth, profitability, and customer acquisition economics. Design experiments, develop forecasts and performance models, build measurement frameworks, investigate anomalies, and provide actionable recommendations. The role requires translating ambiguous business questions into analytical solutions, communicating insights to technical and non-technical stakeholders, and establishing scalable analytical processes that support strategic decision-making.

Lead the strategic direction and evolution of Netflix’s company-wide Experimentation Platform. Establish rigorous causal inference and experimentation standards, automate trust and validation practices, consolidate fragmented systems, shape platform product roadmaps, and collaborate with data science and engineering leaders. Translate experimentation needs into scalable tools, influence adoption across teams, mentor colleagues, and ensure reliable experiment allocation, measurement, analysis, and reporting.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Leads advanced data analysis to guide product and business decisions, develops metrics, dashboards, data products, and AI-enabled reporting systems, and translates findings into strategic recommendations. Partners with data engineers on dimensional data models, data governance, and quality controls. Mentors analysts, aligns roadmaps with business priorities, improves problem framing, and communicates insights effectively to technical and business stakeholders.

Lead high-impact modeling initiatives using deep learning, LLMs, and traditional methods. Prototype, deploy, and monitor hybrid models on large insurance datasets, drive MLOps standards, and communicate results to nontechnical stakeholders to realize business impact.

Lead end-to-end development and deployment of predictive and hybrid deep learning/LLM models for insurance use cases. Prototype, validate, and productionize models using modern ML frameworks and MLOps, collaborate with product and engineering, and communicate technical results to stakeholders to drive measurable business impact.