Executive Director, AI/ML Drug Discovery Analytics

The Opportunity:

As Executive Director, AI/ML Drug Discovery Analytics, you will be a senior leader shaping the future of data-driven drug discovery at Revolution Medicines. You will define the Research’s AI/ML strategy, oversee implementation of advanced analytics across discovery and build a world-class team to unlock the full potential of our rich internal datasets and make these insights accessible to other scientist. Your leadership will directly influence early discovery innovation, pipeline acceleration, and strategic portfolio decisions.

Responsibilities include:

Technical Leadership and Innovation: You will have a deep understanding of Machine Learning Operations and AI system design.  Design and scale a robust ML infrastructure, integrating MLOps best practices to ensure seamless development, deployment, and monitoring of AI/ML models.

Drive innovation in applying deep learning, generative models, and diffusion models to drug discovery, including target prediction, hit identification, and lead optimization. Leverage RevMed’s unique datasets to generate novel hypotheses and enable data-driven decision-making across the research continuum.

Strategic vision and Leadership: Define the long-term vision for AI/ML-enabled discovery and align it with organizational priorities.  Build, mentor, and inspire a cross-disciplinary team of data scientists, computational chemists, engineers, and biologists.  Establish key performance metrics and ensure delivery of impactful solutions that accelerate portfolio progression.

External and Cross-functional Collaboration: Forge partnerships with leading academic groups, technology innovators, and compute providers to access cutting-edge platforms and methodologies. Collaborate across research, chemistry, biology, and IT to identify high-impact opportunities for AI/ML integration and ensure smooth deployment of scalable solutions.  Act as a thought leader internally and externally, representing RevMed at scientific forums and shaping the broader field of AI-driven drug discovery.  Work closely with the Chief Digital Officer and team to build and drive the research vision for advanced analytics within the enterprise framework.  Lead the cross functional analytics team to harness data science and AI to accelerate early drug discovery.  

Required Skills, Experience and Education:

• Ph.D. or Masters degree in Computer Science, Machine Learning, Physics, Math or a relevant scientific discipline with over 10+ years’ experience, exposure to drug discovery is a plus.

• Expertise in a wide variety of AI/ML-based computational techniques and developing adaptable ML workflows to solve challenging problems.

• Deep understanding of AI/ML techniques such as deep learning, reinforment learning and generative models.

• Expertise in frameworks such as PyTorch/TensorFlow, training, and fine-tuning models on GPUs.

• Track record of deploying AI/ML solutions at scale.

• Proven track record of leadership and cross-functional collaboration across ML-Scientist, Software Engineering, and MLOps.

• Previous experience leading large AI/ML projects that require engaging with collaborators across the board and with varying degrees of expertise.

• Expertise in machine learning infrastructure and MLOps, including cloud and on-prem compute environments.

• Demonstrated ability to build, scale, and lead high-performing cross-functional teams.

• Strong communication and leadership skills with the ability to bridge diverse scientific and technical disciplines.

• Passion for scientific innovation and a relentless commitment to improving patient outcomes.

Preferred Skills:

• Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.

• Knowledge of oncology therapeutic area and drug discovery.

• Strong familiarity with biological and chemical datasets and compound libraries.

• Evidence of successful coaching, mentorship and development of both individuals and teams in order to build long-term organizational capability.

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