Senior Data Scientist
New
B
BlueFlagFederal healthcare AI
Workable workplace: remote; Workable locations: Salt Lake City, Utah, United States. Raleigh, North Carolina, United States. Austin, Texas, United States. Dallas, Texas, United States. Pittsburgh, Pennsylvania, United StatesFull-TimeSenior
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 15+ years of experience (or commensurate experience); desired: 5+ years as a data scientist
- Required Skills
- PythonSQLGitMLFlowNumpyPandasDatabricksscikit-learn
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.
- Proficiency in Python, including pandas, NumPy, SciPy, and scikit-learn.
- Advanced SQL skills, including window functions, CTEs, and query tuning.
- At least 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, including dataset assessment, production deployment, experiment tracking, and model management with MLflow or similar.
- Grounding in supervised and unsupervised machine learning, gradient-boosted trees, model selection, cross-validation, and hyperparameter tuning.
- Applied statistics experience, including hypothesis testing, regression, sampling, and experimental design.
- Experience working with large datasets in a distributed environment using Spark or PySpark.
- Sound model evaluation practices, including selecting metrics, handling class imbalance, avoiding leakage, and explaining model behavior.
- Working knowledge of LLMs and agentic AI workflows, including prompt design and RAG patterns.
- Git version control 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 able to obtain a public trust clearance.
Responsibilities
- Consult with internal clients to frame business problems as analytical or ML problems, define success metrics, and set scope.
- Build data products and workflows from source data discovery through production deployment.
- Migrate and modernize legacy R, Stata, and SAS workloads to Python and Databricks.
- Develop, train, and validate machine-learning models for classification, regression, forecasting, clustering, and anomaly detection.
- Engineer features and build reusable, governed feature pipelines using 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 production models for performance, data drift, and data quality, and set review or retraining thresholds.
- Lead client AI adoption, evaluate LLM and agent outputs, and document models for governance and responsible AI review.
- Create reference use cases and training, support client teams, and present findings to technical and non-technical audiences.
View Full Description & ApplyYou'll be redirected to the employer's site
