Data Scientist

Top Skills

• 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.

Required Qualifications

• 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.