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Jobs(38,923)/Staff Scientist Role(152)/adaptyv (10) - Forward Deployed Scientist
adaptyv

adaptyv - Forward Deployed Scientist

Lausanne - Hybrid - Remote3w ago
In OfficeWWArtificial IntelligenceStaff ScientistClosePythonhypothesisExpress

Requirements

• Protein science background. A degree and/or hands-on experience in protein design, computational biology, biochemistry, structural biology, or a closely related field. • Fluent in modern protein design. You understand today's design and prediction models well enough to advise on which to use when — and you keep up as the field moves. • Data-driven. You're comfortable analyzing experimental data (Python, notebooks, basic stats) and drawing clear conclusions from messy, real-world results. • Loves working with people. You're a strong communicator who's energized by solving hard problems alongside customers, not just at a desk. • Scientific problem-solver. When a campaign isn't working, you form hypotheses, design experiments to test them, and iterate — you don't guess. • Autonomous and curious. You take ownership of a customer's success and go deep on whatever the science demands. • Experience working with external partners or customers is a plus, but the core is being a great scientist who likes solving problems in the open. • Application deadline • We are reviewing applicants on a rolling basis.

Responsibilities

• Partner with customers to scope protein design campaigns: define the target, the success criteria, and the experimental plan to get there. • Advise on design strategy — which models and methods (de novo design, diffusion models, protein language models, structure prediction, physics-based tools) fit which problems, and where their limits are. • Help design the right experiments to test a hypothesis: what to express, what controls to include, which assays will actually answer the question. • Analyze experimental results with customers — binding, expression, stability — and translate data into the next round of designs. Deep experience with biophysical characterization techniques like SPR, BLI, nanoDSF etc is a must. • Close the loop: turn each campaign's results into sharper designs and a tighter iteration cycle. • Build trusted relationships that grow from a first project into long-term scientific partnerships. • Feed what you learn back to our product, science, and platform teams so the whole experience gets better.

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