Applied AI Health Data System Engineer-Senior Manager

Preferred Knowledge/Skills

• Demonstrates in-depth level abilities and/or a proven record of success managing the identification and addressing of health system needs 

• Domain expertise in the healthcare value chain including but not limited to Claims, Pharmacy, Finance, Clinical Domains 

• 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.  

• Demonstrates in-depth abilities and/or a proven record of success learning and performing in functional and technical capacities within healthcare data science and AI, including the following areas:  

• Managing GenAI application development teams building healthcare-facing solutions, including back-end LLM orchestration, agentic workflow design, and front-end integration with clinical and operational portals;  

• Using Python (e.g., Pandas, Scikit-learn, Keras, Transformers) and common LLM development frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel) to build healthcare AI solutions; proficiency with relational storage (SQL, including clinical schemas and non-relational storage (NoSQL, vector databases such as Pinecone or Chroma for RAG pipelines);  

• Experience in analytical techniques including Machine Learning, Deep Learning, and Optimization applied to healthcare use cases such as risk stratification, readmission prediction, clinical coding automation, length-of-stay modeling, and staffing/scheduling optimization;  

• Vectorization and embedding of clinical text, prompt engineering for healthcare contexts, RAG (retrieval-augmented generation) workflow development for clinical knowledge retrieval, and design of agentic AI workflows for multi-step healthcare processes such as prior authorization, care gap identification, and revenue cycle task automation;  

• Hands-on experience with Azure (including Azure OpenAI Service, Azure Machine Learning, and Azure Health Data Services), AWS (SageMaker, Bedrock), and/or Google Cloud (Vertex AI) platforms, with an understanding of PHI-compliant deployment patterns and HIPAA-aligned cloud configurations;  

• Experience with data warehouse technology including Snowflake or Databricks 

• Experience working with Anthropic – Claude and Claude code to accelerate development and build applications 

• Experience with Git version control, unit/integration/end-to-end testing, CI/CD, and MLOps practices including model monitoring, performance drift detection, and model governance frameworks appropriate for regulated healthcare environments.