Lead Data Scientist

What you'll work on

• Serve as the technical lead for advanced analytics, predictive modeling, and machine learning on the task order, providing expert direction across all AI/ML initiatives supporting clinical informatics, interoperability, and Agile integration.
• Design, develop, and validate machine learning models and predictive algorithms that support clinical decision support (CDS), Veteran population health management, clinical outcome prediction, and operational efficiency optimization within VA's health IT environment.
• Apply NLP techniques to extract structured clinical information from VA EHR unstructured text (clinical notes, discharge summaries, consult reports), supporting clinical informatics research, data quality assessment, and terminology validation activities.
• Validate algorithm performance against clinical gold standards, ensuring that AI/ML models are explainable, clinically appropriate, and meet VA's responsible AI and algorithmic accountability standards.
• Integrate clinical informatics systems — including VistA/CPRS and Oracle Health Cerner Millennium — into analytical pipelines using Python, R, and applicable clinical interoperability standards including HL7 FHIR APIs.
• Validate data migration fidelity for VistA-to-Cerner transitions by developing statistical validation frameworks that detect mapping errors, data loss, and semantic drift in migrated clinical records.
• Develop and maintain data science deliverables using frameworks including TensorFlow, PyTorch, scikit-learn, and applicable healthcare data science libraries.
• Produce data visualization products using Power BI, Tableau, or equivalent tools to communicate analytical findings to clinical, programmatic, and executive stakeholders.
• Ensure all data science and AI/ML activities comply with VA data governance policies, HIPAA Security Rule, FISMA controls, VA Handbook 6500, and VA's responsible AI framework — with particular attention to the handling of PHI and PII in model training and validation datasets.
• Support SAFe PI Planning by providing data-driven predictive analytics on program performance trends, delivery risk, and clinical informatics quality metrics.

What we're looking for

• Ph.D. in Data Science, Machine Learning, Health Informatics, or a closely related quantitative discipline.
• Certified Health Data Analyst (CHDA) from AHIMA or equivalent healthcare data science credential.
• SAFe Practitioner or Certified Scrum Master (CSM) certification.
• Healthcare data science or AI-specific certifications (e.g., AWS Certified Machine Learning – Specialty, Google Professional Data Engineer, or equivalent).
• Direct experience working with VA data environments including VINCI, VA CDW, or the VA National Artificial Intelligence Institute (NAII) research programs.
• Prior publication or presentation of AI/ML research in peer-reviewed clinical informatics or biomedical informatics venues (e.g., JAMIA, AMIA Annual Symposium).
• Experience with VA's responsible AI governance framework or participation in VA's AI Tech Sprint or similar federal AI innovation programs.
• Experience developing NLP annotation pipelines using UMLS Metathesaurus or SNOMED CT-based concept recognition systems.

If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.