Associate Director, AI Engineering

Overview

Company Description 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 – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio.

For more information about Abb Vie, please visit us at  Follow @abbVie on X, Facebook, Instagram, You Tube, Linked In and Tik Tok.

Data Science & Analytics (DSA) is an ambitious team of Data Scientists with a unique blend of Business, Scientific, and Machine Learning expertise that seeks to unleash the full potential of Abb Vie s data assets by bringing technology and/or data and insights to the forefront of decision making via fit-for-purpose data analytics solutions.

• Lead and drive enablement of GenAI and Agentic AI into Data Science tools & capabilities across different phases of clinical development.
• Lead a team of data scientists engaged in the development of these applications.
• Partner with internal teams and cross-functional stakeholders to capture business needs.
• Collaborate with internal team members to drive testing and rollout of AI solutions and data products.
• Manages a team of associates and data scientists in DSA; may also manage line-level people managers.
• Responsible for coaching, mentoring and performance management of direct reports, as well as talent development in alignment with DSS strategy and R&D business needs.
• Identifies new capabilities and opportunities to drive analytics innovation and value creation. Forms cross-functional teams to implement new capabilities.
• Influences cross-functional stakeholders and Sr. Executives to identify opportunities to unleash the full potential of data and analytics, including advanced ML techniques and Artificial Intelligence.
• Represents clinical analytics at critical governance, quality and compliance, and regulatory meetings.
• Translate R&D strategy into functional area plans. Works with cross-functional leaders to gain alignment on project prioritization and timelines.
• Fosters innovation and an entrepreneurial culture within the team that attracts and retains top talent; creates an environment of continuous learning.
• Responsible for crafting functional area strategies while ensuring alignment with the cross-functional stakeholder groups.
• Ensures adherence to federal regulations and applicable local regulations, Good Clinical Practices (GCPs), ICH Guidelines, Abb Vie Standard Operating Procedures (SOPs), and to functional quality standards.
• Stays abreast of new and/or evolving local regulations, guidelines and policies related to clinical development.

• Bachelor’s or Master s degree in statistics, mathematics, analytics, bioinformatics, data science or equivalent field with 10+ years (BS), 8+ years (MS) or 6+ years (PhD) of related experience.
• Must have 4+ years experience with people management and talent development.
• Intermediate-level proficiency in R, SAS or other statistical packages. Intermediate-level knowledge of statistical and data mining techniques.
• Intermediate proficiency with visualization tools like Spotfire, Tableau or equivalent.
• Demonstrated project management skills.
• Experience with Machine Learning and other advanced analytics techniques preferred.
• Demonstrated effective communication skills. Demonstrated ability to communicate analytical and technical concepts in layman s terms.
• Demonstrated problem-solving and analytical skills. Demonstrated history of successful execution in a fast-paced environment and in managing multiple priorities effectively.
• Demonstrated ability to influence Sr. Executives and cross-functional leaders.
• Good understanding of clinical development functional areas and related processes. Proven track-record with leading complex cross-functional initiatives in Clinical Development.
• Preferred to have 2+ years experience with agentic frameworks such as Lang Graph, OpenAI agents, etc. (including orchestration with tool-use), LLMs, vector databases, and retrieval pipelines (RAG).