Principal Data Scientist - AbbVie

The Analytics and Performance Excellence (APEX) function supports AbbVie's US Commercial organization and is comprised of highly regarded researchers, analysts, data scientists and strategists who are committed to being best-in-class within the biopharmaceutical industry. We serve as a strategic in-house counsel, ensuring that all decisions leverage the key insights that we develop. We continue to build new capabilities and skill sets, encouraging our team members to pull up a chair, be themselves, be creative, speak their minds, and do good work. We are a passionate, diverse, flexible, and inclusive organization with a culture that supports the best ideas, wherever they originate. We are smart, fun, quirky, and innovative – and we'd love for you to join us.

The Principal Data Scientist is the technical lead for a portfolio of advanced analytics products that support personalized commercial engagement and performance optimization. Operating within the APEX Enterprise Advanced Analytics and Innovation team, this role is accountable for the end-to-end technical strategy, model design, methodology, development, and validation of AI/ML and statistical solutions. These solutions enable data-driven decision making across customer engagement, targeting, optimization, measurement, and ongoing performance improvement. The role reports to the Associate Director, Data Science and serves as the primary technical partner to the analytics product owner, translating business outcomes and product requirements into rigorous, scalable, and actionable analytics solutions. The position requires deep expertise in applied machine learning, statistical modeling, experimentation, and omni-channel analytics within a pharmaceutical commercial context.

Key Responsibilities

• Lead a portfolio of advanced analytics capabilities across customer understanding, engagement planning, decision support, and measurement.
• Translate product requirements and business goals into scalable technical solutions, including model design, feature engineering, validation, and deployment.
• Lead the design and development of predictive and inferential models that generate actionable insights on customer behavior, engagement opportunities, and drivers of business performance using complex, multi-source data.
• Design analytics approaches that evaluate cross-channel engagement patterns, interaction effects, and temporal dynamics to inform coordinated customer strategies.
• Own measurement methodologies to measure effectiveness, incrementality, and business impact using experimental and observational methods.
• Partner with BTS, Digital Lab, engineering, and platform teams to build scalable, production-ready analytics and AI/ML solutions.
• Establish best practices in model development, including code quality, documentation, reproducibility, peer review, version control, and methodological rigor.