AI/ML Strategy and Delivery
· Lead complex model development, validation, monitoring, and lifecycle management workstreams across classification, regression, NLP, recommendation, deep learning, and personalization use cases.
· Frame ambiguous commercial business problems into structured analytical approaches, identifying data requirements, methodological options, success metrics, and implementation considerations.
· Guide integration of claims, CRM, digital engagement, EMR, specialty pharmacy, omnichannel, and other commercial behavioral datasets into scalable AI/ML solutions.
· Produce executive-ready technical narratives that explain methodology, performance, limitations, business implications, and recommendations for model adoption or refinement.
Personalization and Decision Frameworks
· Architect reusable decisioning frameworks for next-best-action / next-best-channel, patient identification, HCP targeting, segmentation, and engagement prioritization.
· Design experimentation and measurement approaches, including A/B testing, control groups, uplift analyses, and KPI frameworks to quantify business impact.
· Partner with DD&T, Omnichannel, Marketing Operations, and analytics teams to operationalize model outputs into business workflows while preserving quality and traceability.
· Lead evaluation and prototyping of GenAI and LLM-based solutions for insight synthesis, content support, literature and knowledge retrieval, and intelligent assistants for analytics teams.
· Define evaluation criteria, quality gates, and documentation standards for GenAI pilots so outputs are transparent, reliable, and aligned with approved guardrails.
· Convert successful prototypes into reusable analytical assets, prompts, code patterns, and implementation playbooks that can be leveraged across brands and markets.
COE Excellence and Methodology Standards
· Provide technical guidance, code review, model review, and methodology coaching to Senior Analysts and Analysts across assigned workstreams.
· Establish and maintain best-practice libraries, reusable modeling templates, validation checklists, and documentation standards for the Decision Science & AI Enablement COE.
· Contribute to COE capability building by sharing emerging methods, automation opportunities, and practical applications of AI/ML and GenAI in commercial pharma analytics.