Director, Cheminformatics and Predictive Modeling

Role Summary

Lead Kymera’s computational chemistry and predictive modeling strategy to accelerate drug discovery. Drive ML-driven design for potency, selectivity, ADME, and safety; integrate with molecular modeling and generative design to scale computational discovery. Collaborate across functions to define AI/ML roadmaps that accelerate the DMTA cycle and data-driven decisions. Build robust data capture and modeling infrastructure with IT/Informatics and Research teams.

Responsibilities

• Develop and deploy machine learning models for potency, selectivity, ADME, and safety endpoints to accelerate the delivery of novel therapies across various targets
• Integrate machine learning with molecular modeling and generative design approaches to expand the scale of computational molecular design engine in potency and property optimization
• Partner cross-functionally to define and execute AI/ML strategies to accelerate the DMTA cycle and data-driven decision-making processes
• Collaborate closely with IT/Informatics and Research teams to ensure robust data capture, analysis pipelines, and scalable molecular and predictive modeling infrastructure
• Advance computational discovery and design capabilities by evaluating, piloting, and adopting novel and emerging AI and modeling technologies

Qualifications

• PhD or equivalent experience in Computational Chemistry, Cheminformatics, Machine Learning, or a related field
• 8+ years of experience in biotech or pharma applying ML to drug discovery problems
• Deep hands-on experience with ML methods applied to molecular or chemical data
• Strong understanding of chemical representations, structure–property relationships, and ADME/Tox principles
• Proven track-record of successful application of ML in molecular design
• Experience collaborating with cross-functional scientific teams
• Experience with generative models, active learning, and multi-objective optimization
• Strong understanding of cloud-based ML infrastructure
• Track record of publications, patents, and impacts in drug discovery