Principal Research Scientist I Data

AbbVie•Mettawa, IL

9h•$124,500 - $236,500

About The Position

The Principal Data Scientist is the senior technical and scientific leader for AbbVie’s omnichannel engagement analytics program, which uses data and analytics to optimize channel mix, content, field representative call planning, and orchestration. Within the APEX Enterprise Advanced Analytics and Innovation team, this role sets the multi-year science vision for personalized commercial engagement and owns the technical strategy, methodology, and scientific rigor of the AI/ML and statistical solutions supporting it. Reporting to the Associate Director, Data Science, the Principal partners with commercial and analytics leaders to identify valuable problems, articulate how AI can create measurable business value, and work with product owners to deliver scalable solutions. The role combines hands-on expertise in applied machine learning, generative AI, causal inference, and experimentation with the consultative ability to align complex stakeholder groups. It has no formal direct reports, but provides technical leadership and mentorship across the data science community and may lead technical workstreams with external analytics vendors and partners. The position requires a bachelor’s degree in a quantitative discipline and at least eight years of relevant experience, or six years with a PhD. A master’s or PhD in a quantitative field is strongly preferred. The annual base salary range is USD 124,500–236,500. Benefits include paid time off, medical, dental and vision insurance, and a 401(k) for eligible employees; the role is also eligible for long-term incentive programs.

Requirements

Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline.
At least 8 years of experience in data science, machine learning, or applied science, or at least 6 years with a PhD; demonstrated delivery of production-quality models with measurable business impact.
Experience setting technical direction for a portfolio of AI/ML solutions and influencing senior business leaders.
Experience building personalization, recommendation, decision-optimization, or marketing-science solutions.
Deep hands-on expertise in causal inference and experimentation, including A/B testing and quasi-experimental methods.
Hands-on experience using generative AI and AI-assisted development tools, such as LLM APIs, AI coding assistants, or agent frameworks, with concrete examples to discuss in interviews.
Expert proficiency in Python and SQL; experience with deep learning frameworks such as PyTorch or 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.

Nice To Haves

Master’s or PhD in a quantitative field.
Commercial analytics experience in pharmaceutical, biotech, healthcare, or life sciences, including familiarity with IQVIA, Symphony Health, APLD, or similar prescription, claims, and engagement data.
Experience in omnichannel analytics, including journey analysis, touchpoint measurement, closed-loop measurement, or next-best-action for HCP or patient engagement.
Experience with advanced machine learning and optimization methods, including reinforcement learning, multi-armed bandits, transformers, and methods 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.

Responsibilities

Own the multi-year data science vision and technical roadmap for omnichannel engagement analytics, anticipating business needs and developments in AI.
Identify, size, and prioritize opportunities for AI to drive measurable commercial impact; build business cases and influence senior leadership investment decisions.
Use AI-assisted development and agentic tools to prototype, test hypotheses, and demonstrate value; build LLM-backed systems using advanced RAG and context engineering, with rigorous evaluation frameworks.
Develop predictive, recommendation, next-best-action, and sequential decision models using complex, multi-source data to guide customer engagement and resource allocation.
Set standards for measuring effectiveness, incrementality, and business impact using experimental and quasi-experimental methods.
Partner with product owners to define business outcomes, scope, and success metrics, and translate them into scalable technical solutions.
Build alignment among brand and marketing leaders, field sales and operations, Medical, Legal and Regulatory, Compliance, BTS, Digital Lab, and external vendors; clarify problems and resolve competing priorities.
Communicate methods and findings as clear recommendations and compelling narratives for executive and non-technical audiences.
Partner with BTS, Digital Lab, engineering, and platform teams to deploy, monitor, and manage AI/ML solution lifecycles.
Establish best practices for model development, code quality, reproducibility, peer review, and responsible AI.
Stay current in machine learning, generative AI, and causal inference; bring new ideas to the team, encourage analytical debate, and represent AbbVie’s perspective internally and in the broader analytics community.
Provide technical leadership and mentorship across the data science community; lead technical workstreams with external analytics vendors and partners as needed, including reviewing deliverable quality and validating methodology.