Principal Research Scientist

Job Description

The Principal Data Scientist is the senior technical and scientific leader for AbbVie's omnichannel engagement analytics, a data- and analytics-driven engagement optimization program spanning channel mix, content, field representative call planning, and orchestration. Operating within the APEX Enterprise Advanced Analytics and Innovation team, this role sets the multi-year science vision for personalized commercial engagement and is accountable for the technical strategy, methodology, and scientific rigor of the AI/ML and statistical solutions that power it. Reporting to the Associate Director, Data Science, the Principal is a thought partner to commercial and analytics leadership: shaping which problems are worth solving, forming and defending a point of view on how AI creates measurable business value, and partnering with product owners to turn that vision into scalable solutions. The role combines deep, hands-on expertise in applied machine learning, generative AI, causal inference, and experimentation with the consultative skill to align a complex stakeholder ecosystem around the right answer.

Key Responsibilities

• Define the Data Science vision: Own the multi-year Data Science vision and technical roadmap for omnichannel engagement analytics, anticipating where both the business and the field of AI are heading.
• Shape how AI creates business value: identify, size, and prioritize opportunities where AI can drive measurable commercial impact, build the business case, and influence senior leadership decisions on where to invest.
• Lead hands-on with Gen-AI: Personally use AI-assisted development and agentic tools to prototype, test hypotheses, and demonstrate value quickly; build LLM-backed systems using advanced RAG and context engineering, paired with rigorous scientific evaluation frameworks to scale highly reliable omnichannel recommendations.
• Lead the design of predictive and causal models: Develop next-best-action, recommendation, and sequential decision models that inform customer engagement and resource allocation using complex, multi-source data.
• Own measurement science: set the standard for measuring effectiveness, incrementality, and business impact using experimental and quasi-experimental methods, so that AI investments are evaluated rigorously.
• Partner with product owners to shape problems, not just fulfill requirements: co-define business outcomes, scope, and success metrics, and translate them into scalable technical solutions.
• Navigate a complex stakeholder ecosystem: build alignment across brand and marketing leaders, field sales and operations, Medical, Legal and Regulatory (MLR), Compliance, BTS, Digital Lab, and external vendors; reframe requests into well-posed problems and resolve competing priorities.
• Communicate with executive impact: translate complex methods and findings into clear recommendations and compelling narratives for executive and non-technical audiences.
• Productionize at scale: Partner with BTS, Digital Lab, engineering, and platform teams to deploy, monitor, and manage the lifecycle of AI/ML solutions.
• Raise the bar for scientific rigor: establish best practices in model development, code quality, reproducibility, peer review, and responsible AI across the data science community.
• Be a thought leader: stay at the forefront of research and practice in machine learning, generative AI, and causal inference; bring new ideas into the team; provoke rigorous analytical debate; and represent AbbVie's point of view internally and in the broader analytics community.

Supervisory / Management Responsibilities

This role does not carry formal direct report responsibilities but is expected to act as a technical leader and mentor across the broader data science community, raising the bar on scientific rigor and hands-on AI practice. The Principal Data Scientist may lead technical workstreams with external analytics vendors and partners, including oversight of deliverable quality and methodology validation.

Key Competencies

• Scientific Engineering Depth: Deep expertise in machine learning, causal inference, and generative AI, combined with strong engineering discipline in reproducibility, deployment, and enterprise integration.
• AI Fluency and Bias to Action: Uses AI hands-on in daily work to prototype, analyze, and build; critically evaluates AI output; and turns ideas into working solutions quickly.
• Business Judgment and Problem-Framing: Translates ambiguous business challenges into well-scoped problems, connects technical choices to commercial value, and sets long-term technical direction while staying close to delivery.
• Stakeholder Navigation and Executive Influence: Reads organizational dynamics to build alignment across commercial, regulatory, and technology partners, earning trust as a thought partner to win support from senior leaders.
• Thought Leadership: Challenges conventional thinking and sparks meaningful analytical debate with peers and executives.

Qualifications

Education

…