Computational Medicinal Chemist – CADD & Data Science

Novartis; South Dakota; Hybrid; USD 126,000 - 234,000; Full time

Job summary

Novartis in San Diego is seeking a Senior Expert I, Data Science to advance our CADD efforts in drug discovery. You will join a cross-functional team to apply SAR, structure-based and ligand-based design, and predictive modeling to identify and optimize targets.

This role blends experimental and digital approaches, emphasizing machine learning, physics-based methods and large data analyses, with leadership in cross-disciplinary projects and mechanistic studies to inform development decisions.

Qualifications

• PhD in medicinal chemistry, computational chemistry, cheminformatics, computational biology or related field.
• 2+ years post-graduate experience in drug discovery environment.
• Experience with analogue design and demonstrated impact on discovery.
• Familiarity with computational drug design tools and HPC environments.
• Strong communication and collaboration skills.

Responsibilities

• Design medicinal chemistry using SAR and structure-based/ligand-based design.
• Apply ML and physics-based CADD methods to small molecule discovery.
• Stay current with literature and integrate insights into lead characterization and screening.
• Collaborate with interdisciplinary teams for target ID, lead optimization, and nomination processes.
• Drive hypothesis generation to improve clinical success rates for diverse modalities.
• Lead cross-disciplinary mechanistic studies informing discovery strategies.

Skills

Effective listener

Excellent written and oral comms

Strong team orientation

Data visualization

Education

PhD in medicinal/computational chemistry or related field

Tools

Schrödinger

CCG

OpenEye

Job description

Novartis in San Diego is seeking a Senior Expert I, Data Science to advance our CADD efforts in drug discovery. You will join a cross-functional team to apply SAR, structure-based and ligand-based design, and predictive modeling to identify and optimize targets.

This role blends experimental and digital approaches, emphasizing machine learning, physics-based methods and large data analyses, with leadership in cross-disciplinary projects and mechanistic studies to inform development decisions.