[Hiring] Data Scientist @The Proven Method

Role Description

Data Scientist to support critical missions within Peraton's Risk Decision Group. This role builds and validates predictive and prescriptive models on a greenfield data and AI platform in a high-trust federal environment. Work is hands-on and built to migrate — documented, portable, and transferred to the internal team. Engagement is 1 year with option to extend. Active T5/SSBI clearance required.

• 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.

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.

Preferred Qualifications

• Modeling on federal investigative, vetting, fraud, or insider-threat data.
• Direct experience with FedRAMP, NIST 800-171, CMMC L2, or CUI handling.
• Familiarity with LLM/GenAI workflows — useful for collaboration with a peer document-intelligence workstream, but secondary to the core ML skill set.
• H2O (Driverless AI, H2O-3).
• MLflow, Databricks Asset Bundles, Unity Catalog.
• Fairness / adverse-impact analysis in a regulated or decision-support setting.

Soft Skills

• Self-directed execution against a fixed milestone with minimal oversight.
• Honest reporting of model behavior — comfortable stating what synthetic-data performance does and does not establish about real-world accuracy.
• Collaboration across technical and non-technical teams.
• Clear documentation and active knowledge transfer.