Director, Data Science, Foundation Model AI

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Company Overview
A global biotechnology and life sciences organization is leveraging artificial intelligence and machine learning to accelerate the discovery and development of new medicines. The company integrates AI-first approaches with computational biology, genomics, and data-driven insights to identify therapeutic targets, biomarkers, and innovative treatment strategies. Its teams work collaboratively across research, engineering, and data science to advance scientific understanding and patient outcomes.

Summary
The Director of Data Science for Foundation Model AI will lead a team of machine learning researchers and engineers to develop large-scale foundation models and bespoke AI methods for analyzing complex biological data. This role will drive advances in disease understanding and therapeutic innovation through strategic leadership, technical expertise, and cross-functional collaboration.

Primary Responsibilities

• Collaborate with cross-functional teams to identify high-impact research questions
• Align and prioritize the group’s R&D efforts with business objectives
• Articulate the team’s agenda and demonstrate delivered business value to stakeholders
• Provide technical guidance and oversee the training of large multi-modal foundation models
• Work with diverse data types, including omics, imaging, and text modalities
• Interpret and critically analyze results from AI and machine learning models
• Foster a culture of AI excellence within the organization
• Mentor and develop machine learning researchers and engineers
• Stay current with advances in AI, machine learning, and statistics; propose and pilot new research directions
• Publish findings in conferences and journals; contribute to the scientific community
• Establish and maintain collaborations with academic and industry partners

Required Education, Experience, and Skills

• PhD in Computer Science, Statistics, Physics, Engineering, Mathematics, Data Science, AI/Machine Learning, Computational Biology, Bioinformatics, or related STEM field with 7+ years of full-time experience, or an MS with 10+ years of experience
• Deep technical expertise in classical machine learning, probabilistic models, and causal analysis
• Demonstrated world-class expertise in at least one machine learning or AI sub-area through publications or open-source projects
• Experience leading teams of machine learning researchers and software engineers
• Proficiency in training large models on multi-node, multi-GPU environments
• Experience designing novel architectures for multi-modal foundation models
• Knowledge of post-training foundation models, parameter-efficient fine-tuning, interpretability, and preference optimization
• Strong proficiency in Python and awareness of software engineering best practices
• Experience with deep learning frameworks, particularly PyTorch
• Excellent communication skills and ability to work collaboratively in multi-disciplinary teams
• Interest in life sciences problems and disease biology

Preferred Skills and Experience

• Experience with large transformer-based models
• Knowledge of generative modeling paradigms, including diffusion modeling and flow matching
• Experience or interest in reinforcement learning for reasoning models
• Familiarity with biological data and biological foundation models
• Relevant publications and contributions to AI/ML research communities

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