Top Remote Data Science Jobs in Richmond, VA

The Geospatial Scientist leads data management initiatives, oversees data governance, and ensures quality and compliance of geospatial data for DHS.

As a Senior Data Scientist at TRM, you'll develop models and algorithms to combat blockchain fraud, collaborate with teams, and create interactive visualizations to aid law enforcement. You'll leverage your analytical skills to understand complex data structures and contribute to improving financial security.

Lead development and operationalization of LLMs, agentic and image AI solutions; build multimodal datasets; develop production-grade models, RAG pipelines and agent workflows; run RWE analytics and Bayesian optimization for assay/R&D acceleration; deploy solutions on Azure/Snowflake/Databricks and integrate insights into clinical, regulatory, and product programs.

Lead development and deployment of machine learning and analytics products for mortgage lending. Design data pipelines, ensure data quality, build predictive models, communicate insights, mentor junior staff, and partner with engineering and DevOps for scalable MLOps solutions.

Design, build, and deploy end-to-end production ML systems for audience targeting, lookalike generation, and propensity scoring. Own ML lifecycle from experimentation to production monitoring, leverage distributed/cloud platforms, implement vector similarity and embedding systems, engineer scalable features, and collaborate with engineering and product teams to ensure model quality and operational reliability.

Lead development and operationalization of LLM, agentic, and image AI solutions for diagnostics. Build multimodal datasets, implement RAG and agent workflows, apply ML and statistical methods (DoE, Bayesian optimization, causal/survival/longitudinal), analyze RWE/RWD, and integrate insights across clinical, regulatory, and product teams to drive diagnostic and business impact.

Lead development and operationalization of LLM, agentic, and image AI solutions for diagnostics. Build multimodal datasets, develop production-grade models and RAG/agent pipelines, perform RWE analytics, and integrate insights across clinical, regulatory, R&D, and product teams to drive diagnostic performance and patient impact.

Lead analytics for claim decisioning by framing ambiguous problems, building scalable data models and pipelines, designing metrics, and deploying analytical and ML solutions. Partner cross-functionally to measure decision quality, operational performance, and payment accuracy, iterating quickly to deliver measurable impact.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.