Applied AI Health Data System Engineer-Senior Manager

Senior Manager, Applied AI Health System EngineeringAt PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in artificial intelligence and machine learning at PwC will focus on developing and implementing advanced AI and ML solutions to drive innovation and enhance business processes. Your work will involve designing and optimising algorithms, models, and systems to enable intelligent decision-making and automation.Growing as a strategic advisor, you leverage your influence, expertise, and network to deliver quality results. You motivate and coach others, coming together to solve complex problems. As you increase in autonomy, you apply sound judgment, recognising when to take action and when to escalate. You are expected to solve through complexity, ask thoughtful questions, and clearly communicate how things fit together. Your ability to develop and sustain high performing, diverse, and inclusive teams, and your commitment to excellence, contributes to the success of our Firm.Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:Craft and convey clear, impactful and engaging messages that tell a holistic story.Apply systems thinking to identify underlying problems and/or opportunities.Validate outcomes with clients, share alternative perspectives, and act on client feedback.Direct the team through complexity, demonstrating composure through ambiguous, challenging and uncertain situations.Deepen and evolve your expertise with a focus on staying relevant.Initiate open and honest coaching conversations at all levels.Make difficult decisions and take action to resolve issues hindering team effectiveness.Model and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.The OpportunityAs part of the Applied AI Health System Engineering team, you will lead the development of AI, GenAI, and ML solutions tailored to the complex needs of health system and health plans. As a Senior Manager, you will drive use case development across clinical decision support, population health risk stratification, clinical research, and operational efficiency — translating ambiguous healthcare challenges into production-grade AI solutions. 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.ResponsibilitiesOversee the development of healthcare AI and GenAI solutions, including clinical use case design, analytical modeling, prompt engineering, and RAG pipeline developmentLead large healthcare data science engagements, innovating delivery processes and driving continuous improvement across use case development lifecyclesMaintain operational excellence while engaging health system clinical, financial, and operational leaders at a senior level to align AI initiatives with organizational prioritiesGuide teams in processing clinical notes, claims data, ADT feeds, and other structured and unstructured healthcare data sources for use in AI and LLM-powered solutionsManage daily operations of a global healthcare data science team, overseeing model development, MLOps practices, and model governance across client engagementsContribute 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 optimizationFoster a collaborative environment across clinical, technical, and operational team members to solve complex health system data science challengesMaintain excellence in client service and satisfaction, helping health system clients realize tangible value from AI and ML investmentsWhat You Must HaveBachelor's Degree12 years of experience, with meaningful exposure to healthcare data science, health IT, or AI solution development for health system clientsAt least 6-7 years of experience at a health systemPreferred Knowledge/SkillsDemonstrates in-depth level abilities and/or a proven record of success managing the identification and addressing of health system needsDomain expertise in the healthcare value chain including but not limited to Claims, Pharmacy, Finance, Clinical DomainsManaging 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 portalsUsing 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 optimizationVectorization 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 automationHands-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 configurationsExperience with data warehouse technology including Snowflake or DatabricksExperience working with Anthropic – Claude and Claude code to accelerate development and build applicationsExperience 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 environmentsWhat Sets You ApartDemonstrated experience delivering production AI or GenAI use cases in a health system environment, with measurable clinical or financial outcomesHands-on experience building RAG pipelines or agentic AI workflows against clinical data sources, including EMRExperience with MLOps platforms and model governance practices in regulated, PHI-handling environmentsAbility to translate clinical and revenue cycle workflows into