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.
- Synthesize complex technical findings into clear, actionable insights and recommendations for non-technical stakeholders
- Partner with engineering and platform teams to productionalize AI/ML solutions, including deployment, monitoring, and lifecycle management.
- Stay current with advances in applied machine learning, causal inference, and pharmaceutical analytics, proactively identifying and piloting emerging methods that could enhance the existing and new capabilities.
Supervisory / Management Responsibilities
This role does not carry formal direct report responsibilities at this level but is expected to provide technical mentorship and hands-on guidance to junior and mid-level data scientists supporting the advanced analytics team. The Principal Data Scientist may lead technical workstreams with external analytics vendors and partners, including oversight of deliverable quality and methodology validation.
Key Competencies
- Deep expertise in machine learning, statistical modeling, and causal inference, with a track record of delivering production-quality analytics solutions.
- Strong ability to lead technical execution with Product Owners and cross-functional teams.
- Ability to translate ambiguous business problems into well-scoped technical solutions with clear methods and success metrics.
- Hands-on knowledge of the full data science lifecycle, from exploration and feature engineering to deployment and monitoring.
- Strong software engineering discipline, including testing, documentation, reproducibility, and version control.
- Deep familiarity with pharmaceutical data ecosystems, including HCP, patient, and engagement data.
- Strong communication skills with the ability to explain complex technical concepts to non-technical audiences.
- Strong understanding of AI engineering principles, including deployment, monitoring, and enterprise integration.
- Ability to work across data science, engineering, and platform teams to operationalize solutions at scale.