May 28, 2026For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at LeidosCareersFraud@leidos.com.
University'',''University'','''','''','''','''','''','''','''','''',''Scientific Research'',''Scientific Research'',''310000000 School of Medicine Dean''s Office'',''310000000 School of Medicine Dean''s Office'',''Regular'',''Regular'','''','''','''','''',''Day/1st Shift'',''Day/1st Shift'',''Onsite'',''Onsite'',''false'',''319617'',''319617'',''true'',''319617'',''false'',''Submission for the position: DATA SCIENTIST - (Job Number: T236725)'',''false'',''319617'',''false'',''true''. Applies knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to the development and improvement of processes.
li>Managing development teams in building healthcare AI and GenAI solutions, including analytical modeling, prompt engineering, Python-based development, testing, communication of results to clinical and operational stakeholders, front-end and back-end integration, and iterative use case development with health system clients;
Documenting and analyzing healthcare business processes - across clinical operations, and population health programs - to identify AI and GenAI opportunities, gather requirements, define initial hypotheses, and develop solution approaches tailored to health system workflows;
Collaborating with health system client teams - including clinical informatics, population health, and IT leaders - to understand their business and clinical problems and select the appropriate models, LLMs, and approaches for AI/GenAI use cases;
Designing and solutioning AI/GenAI architectures for health system clients, including RAG-based clinical knowledge retrieval systems, agentic AI workflows for care management and revenue cycle automation, and custom LLM application builds with appropriate PHI safeguards;
Managing teams to process healthcare unstructured and structured data - including clinical notes, discharge summaries, claims records, EHR data, and ADT feeds - for use as LLM context, including embedding of large clinical text corpora, generative SQL query development, and building connectors to EHR back-end databases;
Managing daily operations of a global healthcare data science team on client engagements, reviewing developed models, providing feedback, and assisting in analysis of clinical and operational outcomes;
Directing data engineers and other data scientists to deliver efficient, HIPAA-compliant solutions that meet health system client requirements for clinical, financial, and operational AI use cases;
Leading and contributing to development of proof of concepts, pilots, and production use cases for health system clients - spanning clinical decision support, prior authorization automation, patient risk scoring, workforce optimization, and throughput modeling - while working in cross-functional teams;
Facilitating and conducting executive-level presentations to health system leadership showcasing GenAI and ML solution capabilities, use case development progress, model performance, and recommended next steps;
Structuring, writing, communicating, and facilitating client presentations that translate complex AI and ML concepts into clear clinical and business value narratives for health system audiences; and,
Managing associates and senior associates through coaching, providing feedback, and guiding work performance, with an emphasis on developing healthcare domain knowledge alongside technical AI and ML capabilities. You will architect and build production-grade RAG pipelines, MCP connections, agentic AI workflows, and MLOps frameworks, managing daily operations across global delivery teams while engaging health system leaders at the executive level to ensure measurable clinical and operational impact.