Senior Data Scientist

Job Description

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
Why Join BlueFlag
At BlueFlag, we're passionate about leveraging cutting-edge technology to make a real difference. You'll be at the forefront of cloud innovation, working on projects that directly impact people's lives. We offer a high-growth, entrepreneurial environment that values fresh ideas and authentic teamwork.
If you're ready to take your data engineering career to new heights and contribute to meaningful projects that push the boundaries of technology, we want to hear from you. Join BlueFlag and be part of a team that's shaping the future of AI solutions!

Benefits
Competitive salary

Generous annual leave and paid holidays

Comprehensive group health and dental plans

401(k) with company match

Life insurance and AD&D coverage

Ongoing training and professional development opportunities

Minimum Qualifications

Bachelor's degree in Engineering, Computer Science, Statistics, Mathematics, Systems, Business, or a related scientific or technical discipline, and 15+ years of experience (or commensurate experience) Proficient in Python (pandas, NumPy, SciPy, scikit-learn) and advanced SQL (window functions, CTEs, query tuning) for data analysis 2+ years of hands-on work on a leading cloud data platform such as Databricks, Azure, AWS, or GCP Experience across the end-to-end data science workflow, from finding and assessing datasets to production deployment, including experiment tracking and model management with MLflow or similar Solid grounding in traditional machine learning: supervised and unsupervised methods, gradient-boosted trees (XGBoost, LightGBM), model selection, cross-validation, and hyperparameter tuning Strong applied statistics: hypothesis testing, regression, sampling, and experimental design Experience working with large datasets in a distributed environment (Spark/PySpark) Sound model evaluation practice: picking the right metrics, handling class imbalance, avoiding leakage, and explaining model behavior (for example SHAP or feature importance) Working knowledge of large language models (LLMs) and agentic AI workflows, including prompt design and RAG patterns Version control with Git and collaborative development practices (code review, branching, testing) Ability to explain technical work to non-technical stakeholders and turn ambiguous requests into defined deliverables

Requirements

Bachelor's degree in Engineering, Computer Science, Statistics, Mathematics, Systems, Business, or a related scientific or technical discipline, and 15+ years of experience (or commensurate experience)
Proficient in Python (pandas, NumPy, SciPy, scikit-learn) and advanced SQL (window functions, CTEs, query tuning) for data analysis
2+ years of hands-on work on a leading cloud data platform such as Databricks, Azure, AWS, or GCP
Experience across the end-to-end data science workflow, from finding and assessing datasets to production deployment, including experiment tracking and model management with MLflow or similar
Solid grounding in traditional machine learning: supervised and unsupervised methods, gradient-boosted trees (XGBoost, LightGBM), model selection, cross-validation, and hyperparameter tuning
Strong applied statistics: hypothesis testing, regression, sampling, and experimental design
Experience working with large datasets in a distributed environment (Spark/PySpark)
Sound model evaluation practice: picking the right metrics, handling class imbalance, avoiding leakage, and explaining model behavior (for example SHAP or feature importance)
Working knowledge of large language models (LLMs) and agentic AI workflows, including prompt design and RAG patterns
Version control with Git and collaborative development practices (code review, branching, testing)
Ability to explain technical work to non-technical stakeholders and turn ambiguous requests into defined deliverables

US Citizen:
Must be a citizen of the United States

Security Clearance:
Must be able to obtain a public trust clearance. Must be eligible to work in the United States.
Desired
5+ years as a data scientist, shipping multiple products that run in operation
Working experience with Databricks in Azure, including Unity Catalog, Delta Lake, Databricks Jobs, and Databricks notebooks/Repos
MLOps experience across the lifecycle, such as:
Model Serving endpoints for real-time inference
Feature engineering and feature tables governed in Unity Catalog
CI/CD for ML with Azure DevOps or GitHub Actions, and Databricks Asset Bundles
Production monitoring for drift and model quality (for example Lakehouse Monitoring)
Champion/challenger or A/B testing of models in production
Automated retraining and model lineage and auditability
Experience with Azure AI services (Azure OpenAI, Azure Machine Learning) or Mosaic AI (Vector Search, Agent Framework)
Prior experience shipping products that use LLMs or AI agents, including evaluation and guardrails
2+ years building visual insights (dashboards and reports) that support operational needs, with Power BI, Databricks AI/BI dashboards, or Tableau
Prior experience with R, Stata, or SAS
Experience refactoring R, Stata, or SAS codebases to Python, including validating that results match
Experience with VA or federal healthcare data, and handling PHI/PII under federal privacy and security requirements
Familiarity with federal AI governance and responsible AI practices (bias testing, model documentation, human oversight)
Experience training or mentoring analysts and data scientists
Master's or PhD in a quantitative field