PwC - Senior Manager - GenAI/AI Architecture - Healthcare/Pharma/Lifescience Domain

Responsibilities:

- Leading GenAI and AI architecture workstreams for client engagements, translating business objectives into practical solution designs.

- Advising stakeholders on AI strategy, use case selection, and delivery approaches across data, analytics, and automation programs.

- Designing target-state AI architectures that integrate data pipelines, model layers, and digital tooling across complex environments.

- Guiding the development of machine learning, deep learning, and natural language processing solutions for business problem solving.

- Overseeing exploratory analysis, statistical modeling, and predictive modeling to inform technical direction and investment choices.

- Coordinating technical teams through implementation planning, issue resolution, and delivery across multiple workstreams.

- Applying systems thinking to identify dependencies, data quality gaps, and architecture risks that may affect solution performance.

- Reviewing AI workflows, model outputs, and data products to validate alignment with client needs and delivery objectives.

- Coaching team members on AI fluency, analytical thinking, and rapid experimentation methods that support delivery execution.

- Communicating solution tradeoffs, status updates, and recommendations to senior stakeholders in clear, concise language.

What Sets You Apart:

- Preference for at least one of the following fields of study: Artificial Intelligence and Robotics, Business Analytics, Computer and Information Science, Computer Engineering, Computer Programming, Data Processing/Analytics/Science, Engineering, Information Technology, Machine Learning, Management Information Systems, Mathematics, Statistics, Systems Engineering.

- Demonstrating GenAI architecture design across client-facing programs.

- Applying systems thinking to uncover root causes and opportunities.

- Crafting executive-ready narratives for diverse stakeholder groups.

- Coaching teams through ambiguity and complex technical decisions.

- Validating AI outcomes and refining solutions from feedback.