PwC is a leading professional services firm, and they are seeking a Senior Manager for their Applied AI Health Data System Engineering team. This role involves leading the development of AI and ML solutions tailored to healthcare needs, driving use case development, and managing global delivery teams to ensure measurable clinical and operational impact.

Responsibilities:

Oversee the development of healthcare AI and GenAI solutions, including clinical use case design, analytical modeling, prompt engineering, and RAG pipeline development
Lead large healthcare data science engagements, innovating delivery processes and driving continuous improvement across use case development lifecycles
Maintain operational excellence while engaging health system clinical, financial, and operational leaders at a senior level to align AI initiatives with organizational priorities
Guide teams in processing clinical notes, claims data, ADT feeds, and other structured and unstructured healthcare data sources for use in AI and LLM-powered solutions
Manage daily operations of a global healthcare data science team, overseeing model development, MLOps practices, and model governance across client engagements
Contribute to the creation of healthcare AI proof of concepts, pilots, and production use cases spanning clinical decision support, revenue cycle, population health, research (including images and genomics) and operational optimization
Foster a collaborative environment across clinical, technical, and operational team members to solve complex health system data science challenges
Maintain excellence in client service and satisfaction, helping health system clients realize tangible value from AI and ML investments

Requirements:

Bachelor's Degree
12 years of experience, with meaningful exposure to healthcare data science, health IT, or AI solution development for health system clients
At least 6-7 years of experience at a health system
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
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

Senior Middleware / Integration Engineer

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