About the Role :
You will lead AI-native product strategy and execution for next-generation molecular modeling, predictive simulation, and ML-powered discovery platforms. Your work will accelerate hit-to-lead, lead optimization, and ADMET prediction capabilities for global pharma and biotech customers.
Key Responsibilities :
- Drive end-to-end product lifecycle for ML-powered drug discovery products-ideation, requirement design, prioritization, technical scoping, and launch.
- Partner with computational chemists, AI scientists, medicinal chemists, and software engineers to build ML workflows for molecular property prediction, protein-ligand scoring, binding affinity estimation, and generative molecular design.
- Develop product strategy for integrating deep learning models (GNNs, transformers, diffusion models) into Schr- dinger-style physics-based simulation platforms.
- Define functional requirements for seamless integrations into desktop, web, and cloud environments used by discovery teams.
- Translate scientific research prototypes into production-ready features with enterprise-grade reliability, scalability, and UX.
- Collaborate with GTM and scientific solution teams to drive customer adoption, design pricing models, and position AI / ML features in the competitive landscape.
- Use product analytics to optimize model performance, user workflows, latency, and platform engagement.
- Ensure compliance with data governance, scientific rigor, and reproducibility standards for ML-assisted decision-making.
Required Qualifications :
6+ years in Product Management with at least 3+ years in AI / ML for life sciences, computational chemistry, or scientific SaaS.Strong understanding of QSAR models, GNNs, AutoML, ADMET prediction, virtual screening, and cheminformatics.Technical fluency with Python, cloud workflows (AWS / GCP Azure), ML pipelines, APIs, and microservice architectures.Ability to collaborate deeply with researchers and translate scientific requirements to product roadmaps.(ref : hirist.tech)