Principal Data Scientist

AbbVie•Irvine, CA

5d•$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 within the APEX Enterprise Advanced Analytics and Innovation team. The role sets the multi-year science vision and technical roadmap for personalized commercial engagement, and is accountable for the technical strategy, methodology, and scientific rigor of the AI/ML and statistical solutions that support it. Reporting to the Associate Director, Data Science, the Principal partners with commercial and analytics leadership to identify valuable problems, shape the business case for AI, and work with product owners to develop scalable solutions. The position combines hands-on expertise in applied machine learning, generative AI, causal inference, and experimentation with the ability to align a complex stakeholder ecosystem. The position has no formal direct reports. It serves as a technical leader and mentor across the data science community and may lead technical workstreams with external analytics vendors and partners, including oversight of deliverable quality and methodology validation.

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.
Track record of delivering production-quality models that drove measurable business impact.
Experience setting technical direction for a portfolio of AI/ML solutions and influencing senior business leaders’ decisions.
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, to prototype and deliver solutions; candidates should be able to discuss concrete examples during interviews.
Expert proficiency in Python and SQL.
Experience with deep learning frameworks such as PyTorch or TensorFlow and with 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.
Scientific and engineering discipline in reproducibility, deployment, and enterprise integration.
Ability to critically evaluate AI output and turn ideas into working solutions quickly.
Ability to translate ambiguous business challenges into well-scoped problems, connect technical choices to commercial value, and set long-term technical direction while remaining close to delivery.
Ability to build alignment and trust across commercial, regulatory, and technology partners and influence senior leaders.

Nice To Haves

Master’s or PhD in a quantitative field.
Commercial analytics experience in pharmaceutical, biotech, healthcare, or life sciences, including familiarity with data sources such as 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 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.

Responsibilities

Own the multi-year Data Science vision and technical roadmap for omnichannel engagement analytics.
Identify, size, and prioritize opportunities for AI to generate measurable commercial impact; build business cases and influence senior leadership investment decisions.
Use AI-assisted development and agentic tools to prototype and test hypotheses, and build LLM-backed systems using advanced RAG and context engineering with rigorous scientific evaluation.
Develop predictive, next-best-action, recommendation, and sequential decision models to inform customer engagement and resource allocation using complex, multi-source data.
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 across 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.
Translate complex methods and findings into 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 the lifecycle of AI/ML solutions.
Establish best practices for model development, code quality, reproducibility, peer review, and responsible AI.
Stay current with machine learning, generative AI, and causal inference research and practice; bring new ideas to the team, foster analytical debate, and represent AbbVie’s perspective internally and in the broader analytics community.
Mentor across the data science community and raise standards for scientific rigor and hands-on AI practice.
Lead technical workstreams with external analytics vendors and partners as needed, overseeing deliverable quality and methodology validation.