WORKING WITH US

Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams rich in diversity. Take your career farther than you thought possible.
Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us .
We seek an enthusiastic and team-oriented scientist to join our Cheminformatics and Machine Learning Team. This individual will directly contribute to small molecule discovery projects that advance drug candidates - from hit identification through candidate nomination.

The successful candidate will perform key research activities, including:

• applying machine learning methods to drive chemical structure optimization within the context of therapeutic projects
• leveraging generative AI to guide molecule ideation in partnership with medicinal chemists
• disseminating successful approaches across the computational chemistry department to enable uptake and impact on several discovery projects

This individual will join a research team with a track record of pipeline impact across modalities and across therapeutic areas. You will be able to perform meaningful research, work with scientists across disciplines, and pursue the discovery and development of life-changing therapies.

Responsibilities:

• Build and deploy deep learning models to predict molecular attributes related to therapeutic action, ADME, and toxicity.
• Explore approaches to combine machine learning and physics-based to drive small molecule design cycles (e.g., active learning)
• Apply data science and informatics methods to analyze chemistry and screening dataset from internal and partner sources.
• Collaborate as a member of cross-functional teams to validate in silico findings
• Communicate results and methods to internal research teams

BASIC QUALIFICATIONS:

Ph.D. and no experience required, OR Master’s Degree with 3+ years of academic / industry experience, OR Bachelor’s Degree with 5+ years of academic / industry experience.

Preferred Experience:

• Ph.D. with machine learning focus in computational chemistry, computer science, or a related field with applications to molecular design. Postdoctoral or industry experience a plus.
• Expertise in programming and scripting languages (e.g., Python) and machine learning frameworks (e.g., PyTorch)
• Expertise in cheminformatics and software and tools (e.g., RDKit, DeepChem) and data science software libraries (e.g., Polars).
• Demonstrated ability to critically assess experimental data and to develop appropriate machine learning solutions
• Strong willingness to collaborate across functional teams in a multidisciplinary work environment.
• Aptitude and desire to learn and apply new techniques is expected as well as personal attributes of integrity, creativity, problem solving, and a strong work ethic.
• Excellent verbal and written communication skills.

If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.

• Build and deploy deep learning models to predict molecular attributes related to therapeutic action, ADME, and toxicity.
• Explore approaches to combine machine learning and physics-based to drive small molecule design cycles (e.g., active learning)
• Apply data science and informatics methods to analyze chemistry and screening dataset from internal and partner sources.
• Collaborate as a member of cross-functional teams to validate in silico findings
• Communicate results and methods to internal research team