Senior Data Scientist

BlueFlag is looking for a Senior Data Scientist to support a premier data and AI platform for the Department of Veterans Affairs. You'll work directly with internal clients to turn operational problems into production machine learning and AI solutions on Databricks in Azure, and help their teams adopt modern cloud tooling. This role is hands-on. We want someone who has taken models from a notebook to production and kept them running.

What You'll Do

Consult with internal clients to frame business problems as analytical or ML problems, define success metrics, and set realistic scope Build data products and workflows that support critical operations, from source data discovery through production deployment Migrate and modernize client workloads from legacy environments (including R, Stata, and SAS) to Python and Databricks, with measurable gains in runtime, cost, or reliability Develop, train, and validate ML models (classification, regression, forecasting, clustering, anomaly detection) for complex business problems Engineer features and build reusable, governed feature pipelines on large datasets with PySpark and SQL Track experiments, register models, and manage model versions and promotion through MLflow Deploy models for batch scoring and real-time serving, and automate retraining with scheduled jobs and CI/CD pipelines Monitor models in production for performance, data drift, and data quality, and set thresholds and alerts that trigger review or retraining Lead AI adoption with clients, including LLM and agentic use cases such as retrieval-augmented generation (RAG), document summarization, and classification Evaluate LLM and agent outputs for accuracy, groundedness, and safety before and after release Document models (purpose, data, assumptions, limitations, evaluation results) so they hold up to governance and responsible AI review Build reference use cases with tutorials, reference code, and training to drive adoption of cloud tooling for concrete business results Host office hours and pair with client analysts and data scientists to upskill them Present findings and model results clearly to technical and non-technical audiences, including leadership