Senior Computational Scientist

Job Description Senior Computational Scientist (Single-Cell + Machine Learning)

We are searching for a Senior Computational Scientist with deep, hands‑on single‑cell expertise to help translate cutting‑edge AI models into impactful discovery and translational workflows. This role sits at the intersection of single‑cell biology, machine learning, and applied computational research, with direct influence on how next‑generation foundation models are evaluated and deployed.

You will partner closely with research scientists, ML engineers, product teams, and external collaborators to turn advanced single‑cell methods into actionable insights for real‑world drug discovery and translational use cases. This is a senior IC position with the opportunity to grow into management as the team scales.

• Lead scientific strategy and execution for evaluating and applying foundation models to single‑cell and multi‑omics datasets.
• Develop, adapt, and benchmark single‑cell workflows, including integration, perturb‑seq analysis, and advanced multi‑modal pipelines.
• Collaborate with research and ML teams to design experiments and rapidly iterate from prototype to insight.
• Work closely with Product and Business Development to translate complex biology + ML capabilities into customer‑facing deliverables.
• Mentor junior scientists and contribute to shaping the culture and scientific standards of a growing computational biology team.
• Bring strong scientific judgment to ensure modeling work remains grounded in translational biology and drug development needs.

• PhD in Computational Biology, Bioinformatics, Machine Learning, or a related field with a strong single‑cell focus, and 2+ years of industry experience; or MSc with 5+ years of relevant industry experience.
• Deep, hands‑on experience with single‑cell technologies (integration, perturb‑seq, multiome, advanced workflows).
• Strong understanding of modern ML/AI approaches for biological data, such as transformers, VAEs, diffusion models, or classical ML methods.
• Proficiency with ML frameworks like PyTorch (or equivalent).
• Excellent communication skills with the ability to explain complex science to diverse technical and non‑technical audiences.
• Comfortable working in a fast‑paced, iterative environment.
• Passionate about building at the intersection of biology, machine learning, and real‑world translational impact.