Senior Data Scientist (Clearance Required)
$113,560–$192,050 year
HybridTysons, Virginia, United States
Job Summary
Design, train, validate, and deploy machine learning models against Army data, moving capabilities from concept to production on LMI platforms. Build production-caliber code and data pipelines that run within the customer's environment, translating ambiguous mission needs into well-scoped analytic problems and clear recommendations for decision-makers. Engineer features and data workflows across structured and unstructured sources, applying MLOps practices to ensure models are auditable and meet federal security standards. Partner with software and DevSecOps engineers to integrate models into deployed systems, including evaluation, retraining, and drift monitoring. Mentor mid-level data scientists and analysts while helping set technical direction for the team.
Required Qualifications
- Active Secret clearance (or eligibility to obtain one)
- U.S. citizenship
- Bachelor's degree in a quantitative field (computer science, statistics, mathematics, engineering, operations research, or related)
- 7+ years of combined experience in data science, statistical modeling, analytics, or data engineering
- Strong programming skills in Python and/or R, and SQL
- Demonstrated experience across the analytic and ML lifecycle, including data preparation, feature engineering, modeling, and validation
- Proven ability to communicate complex results clearly to senior decision-makers
Desired Qualifications
- Advanced degree
- Experience supporting Army or broader DoD customers and familiarity with operating in classified environments
- Active TS/SCI, or eligibility to upgrade clearance
- Experience building, deploying, or maintaining models or data products in production environments, including monitoring and retraining
- Familiarity with common ML libraries (e.g., scikit-learn, PyTorch, TensorFlow) and with MLOps practices, feature stores, or model governance
- Experience with data pipelines and ETL (e.g., streaming or event-driven processing) and with cloud environments (AWS, Azure, or GCP) and containerized workflows (Docker, Kubernetes), or willingness to develop these skills
- Background in NLP, computer vision, time-series forecasting, anomaly detection, or operations research applied to defense problems
- Familiarity with Agile delivery and working on balanced, cross-functional product teams
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