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.

Thanks and Regards,
Murali Sharma