Provides scientific leadership translating foundation models into drug discovery and translational research workflows. Leads model evaluation and applied biology use cases, mentors junior researchers, partners with Product and Business Development, and converts biology and machine learning research into customer deliverables. Requires expertise in computational biology, machine learning for biological data, modern ML frameworks, early drug discovery, scientific communication, and roadmap leadership.
We’re looking for a Senior Computational Scientist to help translate our foundation models into impactful, real-world discovery workflows. In this role, you’ll provide scientific leadership across model evaluation and applied use cases, ensuring our work stays grounded in the most relevant challenges in drug development and translational research.
You’ll join as a senior individual contributor, reporting to the Head of Computational Biology, with the opportunity to grow into management as the team scales. You will act as a key scientific partner to our research team, mentor and support junior colleagues, and work closely with Product and Business Development to turn cutting-edge biology + ML into deliverables that matter for customers.
Requirements
- PhD in Computational Biology, Machine Learning, Bioinformatics, or a related field, with a strong focus on Transcriptomics OR Genomics + ML, and 2+ years of industry (non-academic) experience OR MSc in a relevant field and 5+ years of industry experience applying ML to Transcriptomics OR Genomics problems
- Strong understanding of ML/AI methods for biological data (e.g., transformers, VAEs, diffusion models, classical ML)
- Hands-on experience with modern ML frameworks such as PyTorch (or equivalent)
- Leadership experience with small teams to work towards defined roadmaps or projects
- Excellent communication skills — able to translate complex science into clear, actionable insights for technical and non-technical stakeholders
- Comfortable in a fast-paced, high-iteration environment, moving quickly from prototype → experiment → insight
- Strong passion for building at the intersection of biology and machine learning
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