Data scientist

Build risk-scoring models over synthetic tabular data, engineering features from curated medallion-layer tables. Build anomaly and outlier detection to surface irregularities in records and process data. Build optimization models for prioritization, routing, and resource allocation. Validate honestly - calibration, discrimination, stability, explainability. A correctly characterized model matters more than a flattering headline metric. Package deliverables as jobs and Asset Bundles, tracked in MLflow, and document assumptions, limitations, and what must be revalidated against real data post-ATO.

Strong background building and deploying machine learning models. Experience with: Predictive modeling Classification Clustering Statistical analysis Feature engineering Model evaluation Experience preparing, cleaning, and curating large datasets. Hands-on experience with Databricks preferred. Experience creating synthetic datasets is a plus. Comfortable taking models from concept through production. Looking for candidates who enjoy solving business problems with data and can work independently., U.S. citizenship and active T5/SSBI federally adjudicated clearance required. Hands-on Databricks. Feature engineering on tabular and time-series data - encoding, aggregation, leakage prevention, and selection grounded in domain reasoning rather than automated search alone. Supervised learning on tabular data: gradient boosting (XGBoost/LightGBM), regularized regression, and the judgment to know when the simpler model is the right answer. Model calibration and evaluation under class imbalance - you can explain why AUC alone is insufficient for a risk score. Anomaly detection: isolation forests, autoencoders, statistical process control, or comparable - with a clear account of how you validated detections without labels. Optimization: LP/MIP or heuristic methods (OR-Tools, Pyomo, SciPy, or equivalent) applied to a real allocation or prioritization problem. Explainability (SHAP or comparable) in a decision-support context. Privacy-preserving synthetic data generation from CUI, PII, or comparably restricted source data - relational tabular data with distributional fidelity, cross-column correlations, referential integrity, and preservation of the rare-event structure that anomaly detection and risk scoring depend on. Includes an understanding of re-identification risk. Strong Python, SQL, and Spark. Government or defense contracting experience.