Top Data Science Jobs in Richmond, VA

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.

Lead development of machine learning and NLP solutions for operational risk analytics: partner with cross-functional teams, build end-to-end models (design, training, evaluation, validation, implementation), analyze large numeric and text datasets, deliver insights and reporting to senior leaders, and experiment with emerging generative AI techniques to improve risk management.

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 productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

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.

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 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.

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.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Lead model risk audits across development life cycles: review design, training, evaluation, validation, and implementation of ML models. Partner cross-functionally to surface model risk issues, apply NLP and Generative AI methods, and communicate findings to stakeholders and the Audit Committee.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

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.

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.

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.

The Senior Data Scientist partners with Customer Success to perform deep-dive analyses, diagnose metric shifts, build predictive models (churn/upsell), apply causal inference and experimentation, measure business impact, and develop AI/LLM solutions. Responsibilities include self-serving across the data stack for extraction, pipelining, and dashboards, defining KPIs, and delivering actionable insights to reduce churn, drive revenue, and improve operational efficiency.

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.

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

Lead cross-functional analytics initiatives, develop and deploy advanced statistical and machine‑learning models, build repeatable data pipelines and features, perform deep exploratory analysis, ensure model governance and reproducibility, partner with senior leaders to translate business needs into technical solutions, and contribute to data modernization and cloud migration efforts supporting long‑term care strategy.

Owner of analytics for EHR, revenue cycle, and operations: design and run experiments, analyze financial and operational metrics, investigate billing and account issues, and deliver actionable insights to Product, Operations, Finance, and Customer Experience to improve accuracy, reduce friction, and support growth.