Associate Director - Machine Learning Sciences

Bristol Myers Squibb EU Policy; Cambridge (MA); Hybrid; USD 212,000 - 257,000; Full time

Benefits offered by this job

Flexible Time Off

Paid Holidays

Comprehensive benefits

Job summary

Bristol Myers Squibb EU Policy is seeking an Associate Director to lead the Machine Learning Sciences team in Cambridge. You will set scientific direction, manage a small group of scientists, and partner with pharmacology, DMPK, and data science to ensure ML models improve decision-making across drug discovery.

The role emphasizes staying at the cutting edge of ML methodologies, collaborating across disciplines, and representing the group in leadership forums.

Qualifications

• Advanced degree with extensive experience in chemistry or computational fields and ML applications.
• Proven track record leading teams or projects with measurable scientific impact.
• Strong collaboration across disciplines and ability to communicate complex concepts.

Responsibilities

• Lead and grow the Machine Learning Sciences team focused on ADMET/PK and multiparameter design for small molecules.
• Set scientific strategy and priorities for the ML Sciences team aligned with Computational Sciences and Research goals.
• Partner with diverse scientists to embed ML-driven predictions into project workflows and decisions.
• Drive continuous improvement in model predictivity through validation against experimental data.
• Scan external literature and tech landscape to identify new ML approaches and guide their adoption.
• Represent ML Sciences in cross-departmental forums and communicate results to leadership.
• Mentor team members and foster scientific rigor and collaboration.
• Contribute to departmental strategy as a member of the Computational Sciences leadership community.

Skills

Machine learning

Leadership

Cross-disciplinary collaboration

Communication

Curiosity and initiative

Education

Advanced degree in Chemistry/Computational Chemistry/CS/Engineering

Job description

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.

Position Summary

The Computational Sciences department drives the use of computation—spanning both AI/ML and physics-based methods to accelerate and de-risk the Research Portfolio. Within this department, the Machine Learning Sciences team is a small, high-impact group focused on applying machine learning approaches to strengthen predictive capabilities across our small molecule portfolio. The team's work spans ADMET and PK prediction as well as approaches to multiparameter compound design, and it operates at the interface of computational chemistry, medicinal chemistry, DMPK, in vitro biology, and data science.

We are seeking an Associate Director to lead the Machine Learning Sciences team. This is a high-visibility role reporting to the Executive Director of Small Molecule Computational Sciences, with regular exposure to senior leaders across Research. The successful candidate will set scientific direction for the team, manage a small group of talented scientists, and partner closely with portfolio-facing computational scientists, medicinal chemists, DMPK scientists, in vitro biologists, and data scientists to ensure our predictive models achieve the highest possible accuracy and impact on decision-making.

Beyond managing the team's current portfolio of work, this leader will be expected to keep the group at the leading edge—continuously evaluating new ML methodologies, algorithms, and external technologies, and bringing the most promising approaches into our predictive workflows.

What You’ll Do

• Lead and grow a team of machine learning scientists focused on ADMET/PK prediction and multiparameter design approaches for small molecules.
• Set scientific strategy and priorities for the Machine Learning Sciences team in alignment with broader Computational Sciences and Research goals.
• Partner cross-functionally with portfolio-facing computational scientists, medicinal chemists, DMPK scientists, in vitro biologists, and data scientists to embed ML-driven predictions into project workflows and decision-making.
• Drive continuous improvement in model predictivity through rigorous validation, benchmarking, and iteration against experimental data.
• Scan the external landscape—academic literature, startups, platforms, and emerging technologies—to identify and evaluate new approaches for improving prediction, and lead their assessment and adoption where appropriate.
• Represent Machine Learning Sciences in cross-departmental forums, communicating results, capabilities, and strategy to scientific and senior leadership audiences.
• Mentor and develop team members, fostering a culture of scientific rigor, collaboration, and innovation.
• Contribute to departmental strategy as a member of the Computational Sciences leadership community.

What We’re Looking For

We’re seeking an experienced, innovative scientist that passionate about the role of computation in drug design.

The Ideal Candidate Will Bring

• Advanced degree with experience - (Bachelor’s degree with 12+ years of academic/industry experience, OR Master’s with 10+ years, OR PhD with 8+ years) in Chemistry, Computational Chemistry, Computer Science, Engineering, or a related field. Demonstrated experience applying machine learning methods to chemistry- or drug discovery-relevant problems (e.g., ADMET/PK prediction, property prediction, molecular design).
• A track record of leading teams or projects to productive, measurable scientific impact.
• Strong ability to collaborate across disciplines and communicate complex technical concepts to diverse audiences, including non-computational scientists.
• Demonstrated curiosity and initiative in evaluating and adopting new methods, tools, or technologies.

Preferred Qualifications

• Direct people management experience (supervisory responsibility for scientific staff).
• Experience working within a pharmaceutical, biotech, or similar drug discovery R&D environment.
• Familiarity with multiparameter optimization approaches in small molecule design.
• Experience building or maintaining relationships with external technology providers, academic collaborators, or platform vendors.

We hire for skills and capabilities, not just credentials – if this role excites you, but doesn’t perfectly match your resume, we encourage you to apply anyway.

Compensation Overview

Cambridge Crossing: $211,950 - $256,831

Princeton - NJ - US: $184,300 - $223,325

San Diego - CA - US: $202,730 - $245,665

The starting pay range(s) listed above is for full-time employees (FTE). You may also be eligible for additional discretionary incentive cash and stock opportunities. We determine starting pay thoughtfully – carefully considering the nature of the role, required skills, work location, schedule and the knowledge and experience you bring. Final compensation is guided by pay equity principles and applicable employment laws. Compensation programs are reviewed on an ongoing basis and may be adjusted over time to reflect evolving market factors, and individual, team or Company performance.

Benefits

• Subject to the terms and conditions of the applicable plans then in effect, you may be eligible to participate in our comprehensive benefit plans – including wellbeing support, retirement and financial protection benefits, and insurance offerings (medical, dental, vision, life and disability).
• U.S.-based exempt employees are eligible for Flexible Time Off (FTO), which provides paid time off without a set accrual limit, subject to manager approval, along with 11 paid company holidays each year.
• Non-exempt employees, RayzeBio employees, and employees located in Puerto Rico receive 160 hours of paid vacation annually for new hires (subject to manager approval), 11 paid company holidays, and 3 optional holidays.
• Depending on eligibility, employees may also have access to additional time-off benefits, including paid sick leave, up to two paid volunteer days per year, summer hours flexibility, and leaves of absence for medical, personal, parental, caregiver, bereavement, or military needs. Eligible employees also enjoy an annual Global Shutdown between Christmas Day and New Year's Day.
• U.S.-based job seekers can explore full benefit offerings at https://careers.bms.com/benefits

How We Work

Where you work matters – because collaboration, innovation and patient impact happen in many settings. Our roles are structured across four work models: site-essential, site-by-design, field-based and remote-by-design. The model assigned to this role is based on its core responsibilities. Learn more at https://careers.bms.com/ways-of-working.

Supporting People With Disabilities

BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to [e-mail]. Visit careers.bms.com/eeo-accessibility to access our complete Equal Employment Opportunity statement.

Candidate Rights

BMS will consider qualified applicants with arrest and conviction records, pursuant to applicable laws in your area.

For roles based in Los Angeles County only: If you live in or expect to work from Los Angeles County if hired for this position, please visit this page for important additional information: https://careers.bms.com/california-residents/

Data Protection

We will never request payments, financial information, or social security numbers during our application or recruitment process. Learn more about protecting yourself at https://careers.bms.com/fraud-protection.

Any data processed in connection with role applications will be treated in accordance with applicable data privacy policies and regulations.

If this posting is missing required information required by local law or incorrect, contact BMS at [e-mail] the Job Title and Requisition number. Do not send application-related inquiries to this email. To check your application status, please login to your Candidate Home Account.