Methodology and Analytical Direction
• Lead the selection, design, and evaluation of analytical and machine-learning approaches for IDA initiatives and AI-enabled workflows.
• Design cohort, customer, and healthcare professional segmentation methodologies using appropriate healthcare and real-world data sources.
• Define analytical logic, performance measures, validation methods, and decision criteria for models and agentic workflows.
• Conduct advanced analyses to identify patterns, trends, opportunities, and risks that can inform Medical Affairs priorities and decisions.
• Ensure analytical methods, assumptions, limitations, and outputs are well documented, reproducible, and fit for purpose.
• Evaluate emerging data-science, artificial-intelligence, and large-language-model techniques for relevance, rigor, scalability, and responsible use.
Data, Model, and Solution Development
• Prepare, integrate, explore, and analyze complex structured and unstructured datasets from multiple sources.
• Develop, test, validate, and refine statistical, machine-learning, natural-language-processing, and AI-based models.
• Partner with AI/ML engineers and data engineers to translate analytical methods into reliable, production-ready pipelines and workflows.
• Collaborate with data architecture and governance partners to ensure analytical outputs align with data models, quality standards, privacy requirements, and governance expectations.
• Establish monitoring approaches to assess model performance, data drift, output quality, bias, and continued fitness for use.
• Identify and address data-quality, methodology, or implementation issues that could affect analytical reliability.
Strategic Support and Collaboration
• Provide analytical input to IDA strategic and tactical planning, use-case prioritization, roadmaps, and delivery decisions.
• Translate Medical Affairs and business needs into clear analytical questions, requirements, hypotheses, and evaluation plans.
• Communicate analytical findings, recommendations, uncertainty, and trade-offs clearly to technical and nontechnical stakeholders.
• Partner across IDA and Medical Affairs to ensure solutions are relevant, usable, compliant, and aligned with priority business outcomes.
• Proactively identify new data sources, analytical techniques, and opportunities to improve solution quality, efficiency, and adoption.
• Lead defined projects or analytical workstreams through influence, sound judgment, and effective stakeholder engagement.
Leadership and Capability Building
• Provide technical guidance, mentorship, and coaching to data scientists and other analytical colleagues.
• Promote consistent data-science standards, reusable methods, peer review, documentation, and knowledge sharing across the IDA Team.
• Contribute to an inclusive, collaborative, and change-agile team environment.
• Role-model Pfizer values and behaviors while supporting responsible, transparent, and accountable use of data and AI.