ML Sciences Lead — Small Molecule Design

Bristol Myers Squibb; Cambridge (MA); On-site; USD 184,000 - 257,000; Full time

Benefits offered by this job

Flexible work models

Job summary

Bristol Myers Squibb seeks an Associate Director to lead the Machine Learning Sciences team in Cambridge, MA. The role directs ML initiatives across ADMET/PK, multiparameter design, and cross-disciplinary collaboration with chemistry, biology, and data science teams to drive predictive accuracy and impact.

The successful candidate will shape scientific direction, mentor scientists, and continuously evaluate new ML approaches to stay at the forefront of computational drug discovery.

Qualifications

• Advanced degree with extensive experience in chemistry, computation or related fields.
• Proven track record leading teams or projects with scientific impact.
• Strong ability to communicate complex concepts to diverse audiences.
• Demonstrated curiosity in adopting new ML methods and tools.

Responsibilities

• Lead and grow the Machine Learning Sciences team focused on ADMET/PK prediction and design.
• Set scientific strategy and priorities aligned with Computational Sciences and Research goals.
• Collaborate cross-functionally to embed ML predictions into project workflows.
• Drive model validation, benchmarking, and data-driven improvement.
• Evaluate external ML advances and adopt promising approaches where suitable.
• Represent the team in cross-departmental forums for results and strategy.
• Mentor team members and foster rigor, collaboration, and innovation.
• Contribute to departmental strategy within the Computational Sciences leadership.

Skills

Leadership experience

Interdisciplinary collaboration

Scientific communication

Curiosity and initiative

Education

Advanced degree in Chemistry/Computational Chemistry/CS/Engineering

Job description

Bristol Myers Squibb seeks an Associate Director to lead the Machine Learning Sciences team in Cambridge, MA. The role directs ML initiatives across ADMET/PK, multiparameter design, and cross-disciplinary collaboration with chemistry, biology, and data science teams to drive predictive accuracy and impact.

The successful candidate will shape scientific direction, mentor scientists, and continuously evaluate new ML approaches to stay at the forefront of computational drug discovery.