Applied AI Health Data System Engineer-Senior Manager

The Opportunity

As an Applied AI Health Services Data Science-Senior Manager you will lead client-facing work focused on designing and developing AI-enabled data solutions that transform raw information into usable insights and support intelligent decision-making. As a Senior Manager you will lead large projects, shape delivery approaches, and bring sound judgment, influence, and coaching skills to guide teams through complex work within our Advisory practice. In this role at PwC, you will draw on data and analytics engineering, artificial intelligence, and machine learning skills to develop models, systems, and workflows that support automation, improve business processes, and translate business needs into practical technical solutions.

You will work across multidisciplinary teams, communicate clearly with clients and internal stakeholders, and bring structure to changing priorities while driving operational excellence. The role calls for a strategic mindset, the ability to recognize when to act or escalate, and a focus on developing inclusive teams that can move work forward with composure and accountability.

Responsibilities

• 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;
• Guiding system architecture choices across cloud platforms, model integration patterns, and data pipelines to support scalable AI deployment
• Validating model outputs, evaluation methods, and feedback loops using complex data analysis, AI fluency, and data modeling techniques
• Coaching teams through ambiguous delivery challenges, while directing issue resolution and escalating risks when needed
• Crafting clear status updates, solution narratives, and implementation recommendations for client stakeholders and internal leaders
• 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,
• Identifying improvement opportunities in AI implementation, operational workflows, and platform usage across data, analytics, and automation programs

What You Must Have

• At least a 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

What Sets You Apart

• Preference for at least one of the following fields of study: Analytics/Data Science, Artificial Intelligence/Robotics, Computer Science/Information Systems, Engineering
• At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials
• Demonstrating applied AI system design across health services environments
• Applying systems thinking to data pipelines, models, and platform integrations
• Translating complex findings into clear, client-facing guidance
• Coaching teams through ambiguity, change, and delivery challenges
• 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, perfor