The role
From AbbVie's own posting.
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.
Education
Bachelor’s Degree in Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline required; Master’s or PhD in a quantitative field strongly preferred.
Required Experience
8+ years of experience in data science, machine learning, or applied science (or 6+ years with a PhD), with a track record of delivering production-quality models that drove measurable business impact.
Demonstrated experience setting technical direction for a portfolio of AI/ML solutions and influencing decisions of senior business leaders.
Experience building personalization, recommendation, decision-optimization, or marketing-science solutions, in any industry.
Deep hands-on expertise in causal inference and experimentation, including A/B testing and quasi-experimental methods, to measure business impact.
Demonstrated hands-on experience using generative AI and AI-assisted development tools (e.g., LLM APIs, AI coding assistants, agent frameworks) to prototype and deliver solutions, with concrete examples that can be walked through during the interview process.
Expert proficiency in Python and SQL, with experience in deep learning frameworks (e.g., PyTorch, TensorFlow) and distributed data processing.
Experience productionizing AI/ML solutions in cloud environments, including deployment, monitoring, and lifecycle management.
Track record of leading initiatives across complex, cross-functional stakeholder environments and presenting to executive audiences.
Preferred Experience
Commercial analytics experience in pharmaceutical, biotech, healthcare, or life sciences, including familiarity with data sources such as IQVIA, Symphony Health, APLD, or similar Rx, claims, and engagement data.
Experience in omnichannel analytics, such as journey analysis, touchpoint measurement, closed-loop measurement, or next-best-action for HCP or patient engagement.
Experience with advanced ML and optimization methods, including reinforcement learning, multi-armed bandits, transformers, and other approaches for sequential or recommendation use cases.
Experience building LLM-based and agentic AI systems, including orchestration frameworks, evaluation, and integration through APIs and pipelines.
Experience with cloud ML platforms and MLOps tools such as Azure ML, Databricks, and MLflow.
External visibility through conference talks, publications, patents, open-source contributions, or recognized industry thought leadership.
Experience working in agile product development environments.
Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: 
The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may&#x