Senior Principal Research Scientist, Computational Drug Discovery HitGen​/LeadGen Team Lead

Company Description

Abb Vie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address 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 in our Allergan Aesthetics portfolio.

This position offers an opportunity to make innovative contributions that will define how Abb Vie leverages AI‑enabled drug discovery across our small molecule portfolio. The role reports to the Head of Computational Drug Discovery (CDD) and will lead the computational Hit Gen/Lead Gen (HGLG) team. It will implement and enable close collaboration between members of the HGLG team and partners in Target Enabling Technologies (TET) and Global Medicinal Chemistry (GMEC) teams.

The ideal candidate possesses strong expertise in modern medicinal chemistry, computational and experimental strategies, and property‑based drug design, and has a broad background from early‑stage campaigns, plus an established external footprint and network. Past experience should include mentoring early/mid‑career scientists in matrixed and/or direct reporting relationships.

• Consult with project teams to establish desirable project‑relevant hit/lead criteria and assign an HGLG member to the project team that can execute these goals for accelerated identification of suitable chemical matter.
• Guide HGLG members in benchmarking experiments to evaluate model performance and domain of applicability.
• Communicate clearly with project teams to explain the rationale for selecting specific approaches and how they suit project needs.
• Lead the development of a cohesive collection of cutting‑edge methods and workflows to comprehensively address Abb Vie’s small‑molecule portfolio needs, including binding affinity predictions, pharmacological property predictions, and rapid sampling of both enumerated and AI‑generated chemical spaces.
• Work closely with TET and GMEC leadership teams to identify and address gaps where computational predictions are needed.
• Advance scientific expertise within the HGLG team and collaborate with cross‑disciplinary teams from CDD and Information Research to ensure computational capabilities are best implemented across programs.
• Demonstrate leadership by providing expert advice and shared knowledge with direct reports, peers, and senior management.
• Identify and integrate emerging scientific trends from internal and external sources into functional objectives.
• Present program updates and research strategies to functional and non‑functional leaders, as well as potential and existing partners.
• Maintain awareness of emerging literature and science in the field and author scientific disclosures and publications.
• Maintain a scientific presence outside Abb Vie with appropriate publication and conference abstract submissions highlighting innovative learnings in computational medicinal chemistry.

• Education:
  
  Bachelor’s or equivalent science‑related field with extensive work experience (typically > 18 years);
  Master’s or equivalent with extensive experience (typically > 16 years); or PhD in Science or related field with 10+ years of related work experience.
• Highly organized and self‑directed, with experience leading multiple complex simultaneous research programs or initiatives.
• Extensive knowledge and experience applying data science approaches for drug discovery.
• Extensive knowledge and experience in modern computational approaches for drug discovery.
• Excellent written and verbal communication and critical evaluation skills, including the ability to explain complex ideas to non‑specialists.
• Strong analytical and problem‑solving skills with demonstrated ability to think critically and creatively, providing solutions individually and collaboratively.
• Proven team leadership and management abilities, including experience mentoring, guiding, supervising, and developing scientific personnel with various backgrounds and training levels.
• Highly collaborative, team‑oriented, and considerate of others’ needs.
• Strong interpersonal…