Top Data Science Jobs in Richmond, VA

The role involves leveraging technologies like Python and AWS to develop statistical and machine learning models, translating complex data insights into business goals, and working with a cross-functional team.

As a Senior Associate Data Scientist, you'll partner with teams to analyze model risks, develop machine learning models, and enhance loss forecasting processes at Capital One.

As a Data Scientist, you'll leverage data to drive corporate strategy by building machine learning models and collaborating with cross-functional teams.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Lead development and evaluation of statistical and machine learning models to assess credit risk and challenge production models. Use Python, cloud, and big-data tools to analyze large numeric and textual datasets, partner with cross-functional credit risk and product teams, translate technical findings into business actions, and drive model validation, backtesting, and innovation.

The Data Scientist Manager leads a team in assessing model risks, developing machine learning models, and presenting insights, while leveraging various technologies.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

The Senior Associate, Data Scientist will partner with cross-functional teams to identify risks associated with models and validate machine learning models across the enterprise, enhancing decision-making while ensuring model governance and risk management.

Manage a team to assess model risks, build and validate machine learning models while leveraging advanced technologies and cross-functional collaboration.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Design, build, validate, and deploy ML and NLP solutions (including LLM/GenAI) across audit use cases. Partner with cross-functional teams, analyze large numeric and textual datasets, and translate complex models into business outcomes.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Lead model risk management for Card Intelligence: develop and validate machine learning models end-to-end, define model development standards and change management, collaborate with cross-functional teams, and translate technical results into business goals while using cloud and open-source tools at scale.

As a Principal Associate, Data Scientist, you will develop machine learning models and analytics to combat money laundering, collaborating with a cross-functional team and modernizing risk management practices.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

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.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Lead design and evolution of large-scale nodal power market models. Define mathematical approaches, develop optimization/forecasting methodologies (MILP, SCUC/SCED), collaborate with engineers to productionize models, work with product and customers to translate market problems into software, mentor data scientists, and guide long-term product strategy.

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.

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

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.

Build, train, and evaluate machine learning models; prototype end-to-end solutions; produce analyses, dashboards, and visualizations; document processes; collaborate with product, engineering, and cross-functional teams to productionize models and support data-driven decisions.

Design, build, and deploy production-grade ML models, LLM/RAG applications, and agentic AI systems using Python and cloud platforms. Perform EDA, create dashboards, diagnose model issues, enforce reproducible practices, and collaborate cross-functionally to deliver scalable AI/ML solutions.