Principal Data Scientist

Principal Data Scientist

The Analytics and Performance Excellence (APEX) function supports AbbVie's US Commercial organization and is comprised of highly regarded researchers, analysts, data scientists, and strategists who are committed to being best-in-class within the biopharmaceutical industry. We serve as a strategic in-house counsel, ensuring that all decisions leverage the key insights that we develop. We continue to build new capabilities and skill sets, encouraging our team members to pull up a chair, be themselves, be creative, speak their minds, and do good work. We are a passionate, diverse, flexible, and inclusive organization with a culture that supports the best ideas, wherever they originate. We are smart, fun, quirky, and innovative – and we'd love for you to join us.

The Principal Data Scientist is the technical lead for a portfolio of advanced analytics products that support personalized commercial engagement and performance optimization. Operating within the APEX Enterprise Advanced Analytics and Innovation team, this role is accountable for the end-to-end technical strategy, model design, methodology, development, and validation of AI/ML and statistical solutions. These solutions enable data-driven decision making across customer engagement, targeting, optimization, measurement, and ongoing performance improvement. The role reports to the Associate Director, Data Science and serves as the primary technical partner to the analytics product owner, translating business outcomes and product requirements into rigorous, scalable, and actionable analytics solutions. The position requires deep expertise in applied machine learning, statistical modeling, experimentation, and omni-channel analytics within a pharmaceutical commercial context.

Key Responsibilities

• Lead a portfolio of advanced analytics capabilities across customer understanding, engagement planning, decision support, and measurement.
• Translate product requirements and business goals into scalable technical solutions, including model design, feature engineering, validation, and deployment.
• Lead the design and development of predictive and inferential models that generate actionable insights on customer behavior, engagement opportunities, and drivers of business performance using complex, multi-source data.
• Design analytics approaches that evaluate cross-channel engagement patterns, interaction effects, and temporal dynamics to inform coordinated customer strategies.
• Own measurement methodologies to measure effectiveness, incrementality, and business impact using experimental and observational methods.
• Partner with BTS, Digital Lab, engineering, and platform teams to build scalable, production-ready analytics and AI/ML solutions.
• Establish best practices in model development, including code quality, documentation, reproducibility, peer review, version control, and methodological rigor.
• Synthesize complex technical findings into clear, actionable insights and recommendations for non-technical stakeholders
• Partner with engineering and platform teams to productionalize AI/ML solutions, including deployment, monitoring, and lifecycle management.
• Stay current with advances in applied machine learning, causal inference, and pharmaceutical analytics, proactively identifying and piloting emerging methods that could enhance the existing and new capabilities.

Supervisory / Management Responsibilities

This role does not carry formal direct report responsibilities at this level but is expected to provide technical mentorship and hands-on guidance to junior and mid-level data scientists supporting the advanced analytics team. The Principal Data Scientist may lead technical workstreams with external analytics vendors and partners, including oversight of deliverable quality and methodology validation.

Key Competencies

• Deep expertise in machine learning, statistical modeling, and causal inference, with a track record of delivering production-quality analytics solutions.
• Strong ability to lead technical execution with Product Owners and cross-functional teams.
• Ability to translate ambiguous business problems into well-scoped technical solutions with clear methods and success metrics.
• Hands-on knowledge of the full data science lifecycle, from exploration and feature engineering to deployment and monitoring.
• Strong software engineering discipline, including testing, documentation, reproducibility, and version control.
• Deep familiarity with pharmaceutical data ecosystems, including HCP, patient, and engagement data.
• Strong communication skills with the ability to explain complex technical concepts to non-technical audiences.
• Strong understanding of AI engineering principles, including deployment, monitoring, and enterprise integration.
• Ability to work across data science, engineering, and platform teams to operationalize solutions at scale.

Qualifications

• Bachelor's Degree in Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline required; Master's or PhD in a quantitative field strongly preferred.
• 8+ years of experience in data science, machine learning, or advanced analytics, with delivery of production-quality models and solutions.
• 5+ years of experience in pharmaceutical, biotech, healthcare, or life sciences commercial analytics is highly preferred.
• 4+ years of experience in omnichannel analytics, including journey analysis, touchpoint measurement, engagement optimization, or next-best-action modeling.
• 4+ years of experience measuring commercial effectiveness and business impact using methods such as promotional response analysis, closed-loop measurement, A/B testing, and quasi-experimental techniques.
• Strong proficiency in Python and/or R and strong SQL skills; hands-on Python experience with pandas, NumPy, scikit-learn, and PySpark for data transformation, feature engineering, model development, and scalable workflows.
• Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow for sequential, temporal, or recommendation use cases.
• Experience working with product owners, business stakeholders, and cross-functional teams to translate requirements into technical solutions.
• Experience productionizing AI/ML solutions in cloud environments, including deployment, monitoring, and lifecycle management.

Preferred Experience

• Experience with advanced ML and optimization methods, including reinforcement learning, multi-armed bandits, transformers, and other deep learning approaches for sequential, recommendation, or next-best-action use cases.
• Familiarity with pharmaceutical data sources such as IQVIA, Symphony Health, APLD, or similar Rx, claims, and engagement datasets.
• Experience working in agile product development environments.
• Exposure to cloud ML platforms and MLOps tools such as Azure ML, Databricks, and MLflow.
• Experience presenting analytical methods and findings to senior or executive audiences.
• Exposure to modern AI engineering approaches, including LLM-based workflows, orchestration frameworks, or agentic AI patterns.
• Experience integrating AI/ML solutions using APIs, pipelines, or orchestration tools

Additional Information

• 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 ultimately pay more or less than the posted range. This range may be modified in the future.
• We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.
• This job is eligible to participate in our long-term incentive programs.

AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.

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Pay Range: $124500 - 236500 USD

Where We Work

Role is primarily site- or office-based but can occasionally be performed remotely. Employees who are site/office-based and can occasionally perform their role virtually work both in the office and remotely*, following the policies and regulations in place at their location. US Employees must be in the office on Tuesday, Wednesday, and Thursday with flexibility to work remotely