Scientist - Machine Learning/AI - Cheminformatics )

Bristol-Myers Squibb - Princeton, NJ

Full-time - Mid Level

Hybrid - Princeton, NJ

Chemical Manufacturing

About the position

The Scientist in Machine Learning/AI - Cheminformatics at Bristol Myers Squibb will leverage advanced data science methods, particularly in machine learning, to enhance molecular design and accelerate the drug discovery process. This role involves collaborating with a diverse team to apply cutting-edge techniques from hit identification through candidate nomination, contributing directly to therapeutic projects.

Responsibilities

• Utilize machine learning methods to drive chemical structure optimization for therapeutic projects.
• Collaborate with scientists from various backgrounds to enhance molecular design efforts.
• Participate in high throughput screening and compound profiling to support drug discovery.
• Adopt and implement cutting-edge assay technologies to improve efficiency in drug discovery.
• Communicate findings and methodologies effectively to team members and stakeholders.

Requirements

• Bachelor's Degree with 5+ years of academic/industry experience, or a Master's Degree with 3+ years of experience, or a Ph.D. with no experience required.
• Broad knowledge of modern data science methods with a focus on machine learning.
• Experience in generative molecular design and its applications to therapeutic projects.
• Proficiency in programming languages such as Python, C/C++, and/or R.
• Expertise in machine learning and cheminformatics libraries like TensorFlow, Keras, PyTorch, Pandas, Scikit-Learn, DeepChem, RDKit, or OEchem.

Nice-to-haves

• Ph.D. with 0-2+ years of relevant experience in AI or ML applications to molecular design.
• Postdoctoral or industry experience in cheminformatics or computational chemistry.
• Strong publication record in peer-reviewed scientific journals.

Benefits

• Competitive salary and benefits package.
• Opportunities for professional development and career growth.
• Flexible work environment with options for hybrid work models.