Top Data Science Jobs in Richmond, VA

The Principal Associate, Data Scientist will leverage machine learning technologies and data analysis to drive insights and solutions for a retail banking team, collaborating with cross-functional teams.

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.

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.

Lead development and evaluation of AI products for medical professionals using large-scale datasets. Create product and client-facing analytics, influence data team strategy, and collaborate with product managers, analysts, and engineers to execute data projects from ingestion through analysis to recommendations.

Lead model development and validation across the model lifecycle. Partner with cross-functional teams to build, evaluate, backtest, and implement machine learning models using Python, Spark, H2O, and cloud tools. Translate technical results into business insights and defend models in the Model Risk Office.

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

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.

The Data Scientist will analyze user behavior, create segmentation strategies, design experiments, and track business metrics to drive growth in a SaaS company.

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.

Lead end-to-end data science projects, develop machine learning models, collaborate with stakeholders, mentor team members, and communicate insights effectively.

Design and train LLMs and agentic systems for cybersecurity using RLHF/RLAIF and supervised fine-tuning. Build agent architectures, tooling, retrieval/memory, and evaluation pipelines with rigorous statistical benchmarking. Optimize prompts and inference, collaborate with engineers to productionize prototypes, and drive research on agent planning, safety, and reliability.

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.

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.

Apply machine learning, computer vision, and advanced analytics to business and scientific problems. Build and visualize data-driven solutions, design data science applications, work with cloud and distributed data ecosystems, collaborate across global teams and partners, and deliver deployable ML products in agile teams.

Support biostatistics and data science analyses for healthcare research: clean and manage data, implement statistical and ML methods, build reproducible workflows, create visualizations and reports, collaborate on model development/deployment, and contribute to manuscripts under mentorship.

The Principal Data Scientist will lead data analysis projects, collaborate with product management, assess business impacts, ensure data governance, and mentor junior analysts. Must leverage statistical techniques and machine learning to drive product strategy and business outcomes.

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.

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.

Lead the evaluation and data-quality strategy for advanced AI, vision, and perception systems. Define metrics, failure taxonomies, and human-review protocols; translate evaluation findings into data strategy, experiment priorities, and product release decisions. Partner with model, simulation, and platform teams to drive production-readiness and continuous quality improvements.

Lead strategy and execution for generative and multimodal vision systems, from research to production. Design model architectures, adaptation, evaluation, and deployment pipelines; improve realism, controllability, robustness, and temporal consistency; benchmark and guide model selection; mentor scientists and coordinate cross-functional engineering to ship production-ready AI capabilities.