Note: The job is a remote job and is open to candidates in USA. Scalence L.L.C. is seeking a contract Data Scientist to support critical missions within a high-trust federal environment. This role focuses on building and validating predictive and prescriptive models on a greenfield data and AI platform.
Responsibilities
- Build risk-scoring models over synthetic tabular data, engineering features from curated medallion-layer tables
- Build anomaly and outlier detection models to surface irregularities in records and process data
- Build optimization models for prioritization, routing, and resource allocation
- Validate models through calibration, discrimination, stability, and 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
Skills
- U.S. Citizenship with an Active TS Clearance required
- Hands-on experience with Databricks
- Experience with feature engineering on tabular and time-series data, including encoding, aggregation, leakage prevention, and selection grounded in domain reasoning rather than automated search alone
- Strong experience with supervised learning on tabular data, including gradient boosting (XGBoost/LightGBM), regularized regression, and the judgment to determine when a simpler model is appropriate
- Experience with model calibration and evaluation under class imbalance, with the ability to explain why AUC alone is insufficient for a risk score
- Experience with anomaly detection using isolation forests, autoencoders, statistical process control, or comparable techniques, along with validation approaches when labeled data is unavailable
- Experience with optimization techniques including LP/MIP or heuristic methods (OR-Tools, Pyomo, SciPy, or equivalent) applied to real-world allocation or prioritization problems
- Experience implementing explainability techniques such as SHAP or comparable methods in decision-support environments
- Experience with privacy-preserving synthetic data generation from CUI, PII, or similarly restricted source data, including relational tabular data with distributional fidelity, cross-column correlations, referential integrity, preservation of rare-event structures, and an understanding of re-identification risk
- Strong programming skills in Python, SQL, and Spark
- Government or defense contracting experience
- Experience modeling federal investigative, vetting, fraud, or insider-threat data
- Experience with FedRAMP, NIST 800-171, CMMC Level 2, or CUI handling
- Familiarity with LLM/GenAI workflows to support collaboration with document intelligence initiatives
- Experience with H2O (Driverless AI, H2O-3)
- Experience with MLflow, Databricks Asset Bundles, and Unity Catalog
- Experience performing fairness and adverse-impact analysis in regulated or decision-support environments
Company Overview