Lead Scientist, Computational Drug Design & ML

Bristol-Myers Squibb Co; San Diego (CA); Presencial; USD 157.000 - 190.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Comprehensive benefits

Descripción de la vacante

Bristol Myers Squibb is seeking a Principal Scientist in San Diego to drive computational design for Protein Homeostasis programs. You will apply structure-based and data-driven methods to advance degrader chemistry, build predictive models, and mentor teams across multidisciplinary projects.

Ideal candidates bring deep expertise in computational chemistry, cheminformatics, and machine learning, with proven impact on small molecule discovery in biotech or pharma.

Formación

• Ph.D. with 4+ years of academic or industry experience in computational chemistry or related field.
• Master’s degree with 6+ years of experience in computational sciences, cheminformatics, or related discipline.
• Bachelor’s degree with 8+ years of experience in computational chemistry or related discipline.

Responsabilidades

• Serve as computational scientist on Protein Homeostasis teams, delivering computational objectives.
• Design and triage degrader and molecular glue chemistry, optimizing potency, selectivity, and ADME properties.
• Develop and deploy predictive models and cheminformatics workflows for routine decision-making.
• Integrate structural data with large-scale analyses to deepen SAR insights and shape strategy.
• Curate chemical and biological data resources and ensure reproducibility of analyses.
• Provide scientific leadership and mentor colleagues, raising internal/external visibility.

Conocimientos

Computational chemistry

Cheminformatics

Machine learning

Drug design

Educación

Ph.D. with 4+ years

Master’s with 6+ years

Bachelor’s with 8+ years

Herramientas

Co-crystal structures

Cryo-EM

Molecular modeling

Descripción del empleo

Bristol Myers Squibb is seeking a Principal Scientist in San Diego to drive computational design for Protein Homeostasis programs. You will apply structure-based and data-driven methods to advance degrader chemistry, build predictive models, and mentor teams across multidisciplinary projects.

Ideal candidates bring deep expertise in computational chemistry, cheminformatics, and machine learning, with proven impact on small molecule discovery in biotech or pharma.