Applied AI Health Data System Engineer-Senior Manager

PwC•Tampa, FL

1d•$124,000 - $280,000

About The Position

As an Applied AI Health Services Data Science Senior Manager, you will lead client-facing work designing and developing AI-enabled data solutions that turn raw information into actionable insights and support intelligent decision-making. You will lead large projects, shape delivery approaches, and use sound judgment, influence, and coaching skills to guide teams through complex work in PwC's Advisory practice. The role draws on data and analytics engineering, artificial intelligence, and machine learning to develop models, systems, and workflows that automate tasks, improve business processes, and translate business needs into practical technical solutions. You will work across multidisciplinary teams and communicate with clients and internal stakeholders while bringing structure to changing priorities and driving operational excellence. The position requires strategic thinking, judgment about when to act or escalate, and a focus on developing inclusive teams that deliver with composure and accountability. The work includes healthcare AI and GenAI solution development, architecture, integration, evaluation, and implementation for health system clients. Travel may be up to 80%.

Requirements

At least a bachelor's degree.
At least 12 years of experience, including 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.

Nice To Haves

Study in analytics/data science, artificial intelligence/robotics, computer science/information systems, or engineering.
Certifications aligned to data engineering, machine learning, or cloud platforms, such as AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials.
Applied AI system design in health services environments.
Systems thinking across data pipelines, models, and platform integrations.
Ability to translate complex findings into clear, client-facing guidance.
Experience coaching teams through ambiguity, change, and delivery challenges.
Experience with vectorization and embedding of clinical text, healthcare prompt engineering, RAG workflow development for clinical knowledge retrieval, and agentic AI workflow design 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, including SageMaker and Bedrock; and/or Google Cloud, including Vertex AI, with knowledge of PHI-compliant deployment patterns and HIPAA-aligned cloud configurations.
Experience with data warehouse technologies such as Snowflake or Databricks.
Experience 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 for regulated healthcare environments.
Cloud engineering skills using AWS, Azure, or Google Cloud.

Responsibilities

Manage development teams building healthcare AI and GenAI solutions, including analytical modeling, prompt engineering, Python development, testing, communicating results to clinical and operational stakeholders, front-end and back-end integration, and iterative use case development.
Document and analyze healthcare business processes across clinical operations and population health programs to identify AI and GenAI opportunities, gather requirements, define hypotheses, and develop solutions suited to health system workflows.
Collaborate with clinical informatics, population health, and IT leaders at health systems to understand business and clinical problems and select appropriate models, LLMs, and approaches.
Design AI/GenAI architectures, including RAG-based clinical knowledge retrieval, agentic AI workflows for care management and revenue cycle automation, and custom LLM applications with appropriate PHI safeguards.
Guide architecture choices across cloud platforms, model integration patterns, and data pipelines to support scalable AI deployment.
Validate model outputs, evaluation methods, and feedback loops using complex data analysis, AI fluency, and data modeling techniques.
Coach teams through ambiguous delivery challenges, direct issue resolution, and escalate risks when needed.
Prepare clear status updates, solution narratives, and implementation recommendations for client stakeholders and internal leaders.
Manage daily operations of a global healthcare data science team on client engagements, review developed models, provide feedback, and assist with analysis of clinical and operational outcomes.
Direct data engineers and data scientists in delivering efficient, HIPAA-compliant solutions that meet client requirements for clinical, financial, and operational AI use cases.
Lead and contribute to proof of concepts, pilots, and production use cases, including clinical decision support, prior authorization automation, patient risk scoring, workforce optimization, and throughput modeling.
Facilitate and deliver executive-level presentations to health system leadership on GenAI and ML capabilities, use case progress, model performance, and recommended next steps.
Structure, write, communicate, and facilitate client presentations that explain complex AI and ML concepts as clear clinical and business value narratives.
Identify improvement opportunities in AI implementation, operational workflows, and platform use across data, analytics, and automation programs.