Principal AI and Data Science Lead

Company Description

About Abb Vie

Abb Vie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about Abb Vie, please visit us at  Follow @abbvie on Linked In, ()  Facebook, Instagram () , X ()  and You Tube.

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Job Description

As Data Science and AI Lead overseeing Platform, Governance, and Delivery for Data Science and AI solutions at Abb Vie, you will lead a diverse team of data scientists responsible for designing, executing, and scaling data science solutions across the enterprise. Reporting into Operations Business Insights (OBI), you will establish and execute the vision and roadmap for a robust, agile, and compliant data science platform, ensuring strong governance practices and operational excellence in delivering impactful data science to drive innovation, performance, and agility company wide.

Leading a multifaceted team, you will have end-to-end accountability for platform strategy, stakeholder engagement, project delivery, and talent development. You will be expected to set the standard for technical and consulting excellence, foster a culture of continuous learning, and promote effective collaboration within and outside your group, including functional data scientists, business partners, academic collaborators, and technology leaders.

Key Responsibilities:

- Provide leadership in developing, maintaining, and governing the data science platform to enable secure, scalable, and compliant analytics throughout Abb Vie's global operations, spanning Supply Chain, Quality, Procurement, Manufacturing, and Science & Technology.

- Oversee the delivery lifecycle from requirements discovery, solution design, and modeling through deployment and monitoring of productionized data science applications, studies, and proofs of concept by leading teams of diverse data scientists and analysts.

- Lead and champion best practices in data governance, stewardship, and quality, ensuring compliance with regulatory and organizational standards (e.g., GxP, master data management, data lineage, model validation, etc.).

- Push forward with semantic engineering and agentic engineering, best practices and capability delivery to support more advanced AI use cases. Oversee architects involved in semantic and agentic engineering and solutioning.

- Develop and promote advanced analytics capability, including supervised/unsupervised machine learning, deep learning, AI/ML platform services, and the enablement of novel AI applications such as RAG and semantic layering for human-in-the-loop systems.

- Foster strategic partnerships with senior functional leaders, technology stakeholders, and external collaborators to accelerate the transfer of advanced methods into high-value business solutions.

- Collaborate on group strategy and roadmap, enable cross-functional GenAI initiatives, and serve as a thought leader for education, awareness, and innovation in enterprise data science.

- Ensure robust consulting, communication, and change management plans are in place to drive adoption of data science solutions at all organizational levels, including executive leadership.

- Mentor, coach, and develop a diverse team, building a pipeline of talent that can take on increasingly complex projects and lead education initiatives (journal clubs, technical upskilling, peer review, etc.).

Qualifications

Required:

+ Bachelor's Degree with 9 years' experience;
Master's Degree with 8 years' experience;
PhD with 4 years' experience.

+ Significant experience leading multi-disciplinary teams delivering enterprise-scale data science platforms and applications, preferably in regulated industries such as pharmaceuticals, life sciences, or healthcare operations.

+ Deep expertise in data science methods (ML, AI, statistics), modern data engineering (cloud, big data, pipeline orchestration), and business analytics, with mastery of tools such as Python, R, Dataiku, AWS Sage Maker, Spark, Tableau, etc.

+ Demonstrated ability to define and execute governance frameworks for data quality, master data, model lifecycle management, and regulatory compliance.

+ Extensive consulting and stakeholder management experience, including effective communication of complex technical solutions to executive audiences and cross-functional business partners.

+ Proven ability to drive innovation (strategy, whitepapers, roadmaps), develop cross-functional partnerships, and lead technology transfer initiatives to embed analytics into core business processes.

+ Experience mentoring and growing diverse teams, with a focus on inclusion, equity, and continuous learning.

+ Self-driven, highly adaptable, and able to quickly master new domains and tools in response to…