Principal Data Scientist

Description

The company runs an omnichannel engagement analytics program that uses data and analytics to improve how it plans channel mix, content, field representative call planning, and orchestration.

This principal role sits in the APEX Enterprise Advanced Analytics and Innovation team and reports to the Associate Director, Data Science. You set the multi-year science direction for personalized commercial engagement and own the technical approach, methods, and scientific rigor of the AI/ML and statistical work behind it.

You advise commercial and analytics leaders on which problems to tackle and how AI can deliver measurable business value, and you work with product owners to turn that direction into scalable solutions.

The role calls for hands-on depth in applied machine learning, generative AI, causal inference, and experimentation, plus the consulting skill to get a complex group of stakeholders behind a single answer.

Responsibilities

• Set the multi-year data science vision and technical roadmap for omnichannel engagement analytics, with an eye on where the business and AI are heading.
• Find, size, and rank the places where AI can move commercial results, build the business case, and advise senior leaders on where to invest.
• Build and test prototypes yourself using AI-assisted development and agentic tools, and build LLM-based systems with advanced RAG and context engineering, backed by rigorous evaluation to make omnichannel recommendations reliable at scale.
• Lead the design of predictive, causal, next-best-action, recommendation, and sequential decision models that guide customer engagement and resource allocation, using complex data from many sources.
• Define the standards for measuring effectiveness, incrementality, and business impact through experimental and quasi-experimental methods so AI investments are judged rigorously.
• Work with product owners to frame the problem: agree on business outcomes, scope, and success metrics, then turn them into scalable technical solutions.
• Coordinate across brand and marketing leaders, field sales and operations, Medical, Legal and Regulatory (MLR), Compliance, BTS, Digital Lab, and external vendors. Turn requests into well-defined problems and settle competing priorities.
• Explain complex methods and findings as clear recommendations and stories for executives and non-technical audiences.
• Work with BTS, Digital Lab, engineering, and platform teams to deploy, monitor, and manage AI/ML solutions over their full lifecycle.
• Set team standards for model development, code quality, reproducibility, peer review, and responsible AI.
• Keep up with research in machine learning, generative AI, and causal inference, bring new ideas to the team, challenge analytical thinking, and represent the company's view internally and in the wider analytics community.
• Lead technical workstreams with external analytics vendors and partners, including checking deliverable quality and validating methods.
• This role has no formal direct reports. You act as a technical leader and mentor across the wider data science community.

Requirements

• Bachelor's degree in statistics, mathematics, computer science, engineering, or another quantitative field.
• A master's or PhD in a quantitative field is strongly preferred.
• 8+ years in data science, machine learning, or applied science, or 6+ years with a PhD, including production models that delivered measurable business results.
• A record of setting technical direction for a set of AI/ML solutions and shaping decisions made by senior business leaders.
• Experience building personalization, recommendation, decision optimization, or marketing science solutions in any industry.
• Deep hands-on skill in causal inference and experimentation, including A/B testing and quasi-experimental methods.
• Hands-on use of generative AI and AI-assisted development tools, such as LLM APIs, AI coding assistants, and agent frameworks, to build and ship solutions. You must be able to walk through concrete examples in interviews.
• Expert in Python and SQL, with experience in deep learning frameworks such as PyTorch or TensorFlow and in distributed data processing.
• Experience deploying AI/ML solutions in the cloud, including monitoring and lifecycle management.
• A track record of leading work across complex cross-functional stakeholder groups and presenting to executives.
• Preferred: commercial analytics experience in pharmaceutical, biotech, healthcare, or life sciences, including familiarity with IQVIA, Symphony Health, APLD, or similar Rx, claims, and engagement data.
• Preferred: omnichannel analytics experience, such as journey analysis, touchpoint measurement, closed-loop measurement, or next-best-action for HCP or patient engagement.
• Preferred: advanced ML and optimization experience, including reinforcement learning, multi-armed bandits, transformers, and other sequential or recommendation methods.
• Preferred: experience building LLM-based and agentic AI systems, including orchestration frameworks, evaluation, and API and pipeline integration.
• Preferred: experience with cloud ML platforms and MLOps tools such as Azure ML, Databricks, and MLflow.
• Preferred: public profile through conference talks, publications, patents, open-source work, or recognized industry thought leadership.
• Preferred: experience in agile product development teams.

Conditions

Pay range disclosure applies only in locations with pay transparency laws. The posted range is the base pay the company expects to pay in good faith for this grade, though actual pay depends on factors such as location and may fall above or below it.

This role is eligible for long-term incentive programs.

Bonuses, incentives, and other compensation remain at the company's discretion until paid, and may change under applicable law.

The company is an equal opportunity employer.

Open to candidates in the US and Puerto Rico only.

Benefits

• Paid time off covering vacation, holidays, and sick leave.
• Medical, dental, and vision insurance.
• 401(k) retirement plan.
• Eligibility for long-term incentive programs.