Lead Data Scientist

Leidos•Reston, VA

About The Position

Every day, more than 9 million Veterans rely on VA for their healthcare, supported by a nationwide health IT system. Leidos is supporting this transformation effort, offering an opportunity to make a direct impact on how care is delivered to those who served. The Leidos Health & Services Sector is seeking a full-time Lead Data Scientist to support VA in transforming how it collects, manages, analyzes, and utilizes health data, transforming and improving care for Veterans. The Data Scientist serves as the technical lead for advanced analytics, predictive modeling, and machine learning initiatives that support clinical decision-making and operational optimization across the task order. This individual designs, develops, and validates algorithms that improve interoperability workflows, support clinical informatics system performance, and provide Agile integration reporting to program leadership. VA's health informatics mission increasingly depends on the ability to derive actionable intelligence from large, complex, and heterogeneous clinical data sets — spanning the VA Corporate Data Warehouse (CDW), VistA/CPRS structured and unstructured records, Oracle Health Cerner Millennium data, and enterprise interoperability feeds. The Data Scientist is the program's primary expert in machine learning, natural language processing (NLP), and predictive analytics applied to this environment. This individual also ensures that all AI/ML activities comply with VA data governance, security, and privacy standards — a non-negotiable requirement given the sensitivity of Veteran health data and VA's responsibilities under HIPAA, FISMA, and VA Handbook 6500. The role aligns directly with PWS tasks for predictive analytics, data migration fidelity assessment, and clinical decision support system development.

Requirements

Master's degree in Data Science, Computer Science, Health Informatics, Statistics, or a closely related quantitative discipline.
Minimum 15 years of professional experience in data science or advanced analytics roles.
Minimum 5 years of experience applying machine learning and predictive modeling in healthcare or federal health IT environments.
Expert-level proficiency in Python and/or R with demonstrated experience using ML frameworks including TensorFlow, PyTorch, or scikit-learn.
Strong working knowledge of HL7 FHIR interoperability standards and clinical terminology systems including SNOMED CT and LOINC.
Experience integrating with clinical informatics systems including VA VistA/CPRS and/or Oracle Health Cerner Millennium.
Proficiency with data visualization tools including Power BI and/or Tableau.
Demonstrated experience in Agile environments supporting PI Planning analytics, backlog metrics, and velocity reporting.
U.S. citizenship required; must be eligible for VA Suitability determination and PIV credentialing.

Nice To Haves

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.

Responsibilities

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.