Top Data Science Jobs in Richmond, VA

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.

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.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

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.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

Embedded on a core business team, define and prioritize analytical problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed product, growth, and partnership decisions end-to-end.

Embed with a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end across product, growth, AI/automation, or partnerships.

Embed in a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive outcomes by recommending and executing data-driven actions end-to-end.

Own analytics for an embedded business unit end-to-end: define problems, set metrics, build reports, run analyses and experiments, surface insights, and drive data-informed decisions and outcomes with product, growth, AI, or partnerships teams.

Embed with a business unit to define problems, design experiments, build metrics and reports, run analyses using SQL/Python, surface actionable insights, and drive data-informed decisions end-to-end.

Embedded on a core business team, own end-to-end analytics: define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions across growth, product, AI, and partnerships.

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 predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Analyze and interpret complex data sets, develop predictive models, collaborate with teams, create visualizations, and communicate insights.

As a Data Scientist, you'll build production-grade ML systems for HVAC efficiency, working on scaling models, database optimization, and real-time monitoring systems.

Lead data science initiatives to develop and deploy AI solutions that improve business outcomes. Collaborate with cross-functional teams to analyze data, create models, and evaluate performance.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

Apply advanced analytics, statistical modeling, and machine learning to build and operationalize predictive models for pricing, underwriting, claims, and operations. Partner with business, product, and engineering teams to integrate models, monitor performance, improve data pipelines, and deliver dashboards and insights that inform strategic decisions.

Build lightweight, Slack- and Sheets-integrated AI agents to pull and analyze data from ad and web APIs, surface anomalies and trends, and produce alerts, reports, and LLM-generated content briefs and recommendations. Deliver scoped, fast iterations on multi-step pipelines including classifiers, scorers, and recommendation layers.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Build and deploy NLP solutions: perform EDA on text, design and evaluate models (classification, NER, information extraction), develop pipelines from regex to transformers, apply generative AI and prompt engineering, write production-grade Python, and collaborate with ML engineering to deploy and monitor scalable cloud-based solutions.

Develop and deploy statistical and machine learning models to analyze customer behavior across marketing and digital channels. Design and evaluate experiments/A-B tests, apply causal inference, build scalable Python/SQL pipelines, query Redshift, use SageMaker for production models, maintain version-controlled workflows, and present insights to cross-functional stakeholders.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.