Senior Computational Scientist – AI for Drug Discovery

Key Responsibilities

• Serve as the primary computational lead on drug discovery projects, making AI/ML-derived insights central to critical Go/No-Go decisions rather than supplementary information.
• Establish and implement active learning loops that are realistic for wet-lab execution, statistically sound, and tightly integrated with experimental teams — moving beyond one-way prediction handoffs.
• Translate complex biological and chemical challenges into well-defined computational problems; pinpoint high-impact bottlenecks in the discovery pipeline where AI can meaningfully boost the probability of technical success.
• Collaborate closely with medicinal chemists, DMPK/ADME scientists, and biologists to design multi-parameter optimization strategies that incorporate synthetic accessibility, biological relevance, and ADMET considerations.
• Promote a culture that balances technical rigor with practical impact in drug discovery; mentor junior computational scientists on both model development and effective application to real-world project challenges.
• Evaluate and integrate promising external innovations — from academic research to emerging tools and startups — to strengthen internal capabilities.

Qualifications

• PhD in a quantitative field (e.g., Computer Science, Chemistry, Physics, Biology, or related) with a strong emphasis on molecular or life sciences.
• 3+ years of post-PhD industry experience in a pharmaceutical/biotech drug discovery setting, with demonstrated leadership in driving discovery projects and deep familiarity with the full drug project lifecycle.
• Strong knowledge of medicinal chemistry principles, ADMET/Tox concepts, and pharmacokinetic/pharmacodynamic considerations; ability to diagnose why a model may underperform in practice despite good benchmark metrics.
• Expert-level proficiency in modern AI/ML techniques (e.g., deep learning, generative models, graph-based methods, active learning), combined with practical judgment on when simpler approaches outperform complex ones.
• Record of first-author publications in high-impact journals or conferences relevant to computational chemistry, AI for science, or drug discovery.
• Proven track record where computational work directly influenced molecule progression, project strategy, or key decisions in a discovery program.
• Strong communication skills with a demonstrated ability to convey complex technical ideas and their strategic business value to cross-functional teams and senior leadership.

This role offers the opportunity to lead transformative AI applications in one of the most impactful areas of modern drug discovery.