At 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 Opportunity As 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. 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 What You Must Have - 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 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. What Sets You Apart - Demonstrated experience delivering production AI or GenAI use cases in a health system environment, with measurable clinical or financial outcomes - Hands-on experience building RAG pipelines or agentic AI workflows against clinical data sources, including EMR - Experience with MLOps platforms and model governance practices in regulated, PHI-handling environments - Ability to translate clinical and revenue cycle workflows into structured AI use case requirements and scalable solution designs - Familiarity with Azure OpenAI Service, AWS Bedrock, or Google Vertex AI in a HIPAA-compliant deployment context - Understanding of value-based care, population health program design, or clinical quality measurement and how AI accelerates outcomes in these areas The salary range for this position is: $124,000 - $280,000. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus. PwC offers a wide range of benefits, including medical, dental, vision, 401k, holiday pay, vacation, personal and family sick leave, and more. To view our benefits at a glance, please visit the following link: https://pwc.to/benefits-at-a-glance As PwC is an equal opportunity employer, all qualified applicants will receive consideration for employment at PwC without regard to race; color; religion; national origin; sex (including pregnancy, sexual orientation, and gender identity); age; disability; genetic information (including family medical history); veteran, marital, or citizenship status; or, any other status protected by law. PwC does not intend to hire experienced or entry level job seekers who will need, now or in the future, PwC sponsorship through the H-1B lottery, except as set forth within the following policy: https://pwc.to/H-1B-Lottery-Policy. Learn more about how we work: https://pwc.to/how-we-work For only those qualified applicants that are impacted by the Los Angeles County Fair Chance Ordinance for Employers, the Los Angeles' Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, San Diego County Fair Chance Ordinance, and the California Fair Chance Act, where applicable, arrest or conviction records will be considered for Employment in accordance with these laws. At PwC, we recognize that conviction records may have a direct, adverse, and negative relationship to responsibilities such as accessing sensitive company or customer information, handling proprietary assets, or collaborating closely with team members. We evaluate these factors thoughtfully to establish a secure and trusted workplace for all. Applications will be accepted until the position is filled or the posting is removed, unless otherwise set forth on the following webpage. Please visit this link for information about anticipated application deadlines: https://pwc.to/us-application-deadlines #LI-Hybrid #BI-Hybrid

• Design and oversee data architecture for enterprise applications, ensuring efficient data flow within applications and across integrated systems. • Collaborate with system architects and software engineering teams to define data models, interfaces, and integration strategies that support evolving mission requirements. • Review proposed architectural changes to identify opportunities for improved data sharing, reuse, and interoperability across the enterprise. • Develop logical and physical data architectures that support scalable, maintainable, and secure solutions. • Evaluate how data is collected, processed, stored, and exchanged to ensure optimal performance and long-term sustainability. • Define best practices for application-to-application communication, including REST-based APIs and other integration mechanisms. • Provide technical guidance on structured and unstructured data management, including relational and NoSQL data stores. • Develop prototypes or proof-of-concept solutions when necessary to validate architectural approaches. • Participate in technical design reviews and collaborate with stakeholders to ensure architectural decisions align with program objectives. • Support enterprise modernization efforts by identifying opportunities to improve data accessibility, consistency, and governance.

• Regularly design, develop, and maintain data pipelines that support operational, analytical, and machine learning workloads • Write clean, efficient, and well-documented Python code for data processing and automation tasks • Support the integration and monitoring of AI/ML models in production by delivering reliable, well-structured data pipelines • Communicate technical findings and recommendations clearly to both technical and non-technical stakeholders across teams • Participate in cross-functional meetings and planning sessions to align data engineering efforts with broader business goals • Stay current with advancements in data engineering tools, techniques, and industry-specific applications, particularly within power, oil, and gas environments • Document processes, data models, and systems in a way that supports knowledge sharing and team continuity

• Connect technology talent to real-world projects. • Work in healthy environments with end-to-end professional growth opportunities. • Collaborate with other professionals and leadership on relevant, high-impact projects.