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BlueFlag LLP

Senior Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • A bachelor's degree in Engineering, Computer Science, Statistics, Mathematics, Systems, Business, or a related scientific or technical discipline, and 15 or more years of experience or commensurate experience.
  • Proficiency in Python, including pandas, NumPy, SciPy, and scikit-learn, and advanced SQL, including window functions, common table expressions, and query tuning, for data analysis.
  • At least two 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 tools.
  • Grounding in traditional machine learning, including supervised and unsupervised methods, gradient-boosted trees such as XGBoost and LightGBM, model selection, cross-validation, and hyperparameter tuning.
  • Applied statistics knowledge, including hypothesis testing, regression, sampling, and experimental design.
  • Experience working with large datasets in a distributed environment using Spark or PySpark.
  • Model evaluation skills, including selecting appropriate metrics, handling class imbalance, avoiding leakage, and explaining model behavior using methods such as SHAP or feature importance.
  • Working knowledge of large language models and agentic AI workflows, including prompt design and retrieval-augmented generation patterns.
  • Version control with Git and collaborative development practices, including code review, branching, and testing.
  • Ability to explain technical work to non-technical stakeholders and turn ambiguous requests into defined deliverables.
  • Must be a citizen of the United States.
  • Must be eligible to work in the United States and able to obtain a public trust clearance.

Responsibilities

  • Consult with internal clients to frame business problems as analytical or machine learning problems, define success metrics, and set realistic scope.
  • Build data products and workflows supporting 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 machine learning models for complex business problems, including classification, regression, forecasting, clustering, and anomaly detection.
  • 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 continuous integration and continuous delivery pipelines.
  • Monitor production models for performance, data drift, and data quality, and set thresholds and alerts that trigger review or retraining.
  • Lead AI adoption with clients, including large language model and agentic use cases such as retrieval-augmented generation, document summarization, and classification.
  • Evaluate large language model and agent outputs for accuracy, groundedness, and safety before and after release.
  • Document models, including purpose, data, assumptions, limitations, and evaluation results, for 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.

Desired Qualifications

  • At least five 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 or Repos.
  • MLOps experience across the lifecycle, including model-serving endpoints for real-time inference; feature engineering and feature tables governed in Unity Catalog; continuous integration and continuous delivery for machine learning with Azure DevOps or GitHub Actions and Databricks Asset Bundles; production monitoring for drift and model quality, such as Lakehouse Monitoring; champion/challenger or A/B testing of models in production; automated retraining; and model lineage and auditability.
  • Experience with Azure AI services, including Azure OpenAI or Azure Machine Learning, or Mosaic AI, including Vector Search or Agent Framework.
  • Prior experience shipping products that use large language models or AI agents, including evaluation and guardrails.
  • At least two years building visual insights, including dashboards and reports supporting operational needs, using 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 protected health information or personally identifiable information under federal privacy and security requirements.
  • Familiarity with federal AI governance and responsible AI practices, including bias testing, model documentation, and human oversight.
  • Experience training or mentoring analysts and data scientists.
  • A master's or PhD in a quantitative field.

About the Company

BlueFlag LLP

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