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Job Description Job Title:

Data Scientist Duration:

12+ Months with

Possibility of Extension Location:

100%

Remote Pay Rate:

$80 - $85/hr (All Inclusive)

Clearance:

Active TS Clearance Required Job Description Our client is hiring 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. The work is hands-on and designed for transition-fully documented, portable, and transferred to the internal team. This is a 12+ month contract with the possibility of extension. An Active TS Clearance is required. 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. Required Qualifications 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. Preferred Qualifications Experience modeling federal investigative, vetting, fraud, or insider-threat data. Experience with Fed

RAMP, 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. Soft Skills Self-directed execution against fixed milestones with minimal supervision. Honest reporting of model behavior, including clear communication of what synthetic-data performance does and does not establish about real-world accuracy. Strong collaboration skills across technical and non-technical teams. Excellent documentation and knowledge transfer abilities.

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