Data Scientist III with Security Clearance

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 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. 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.