data
Posted 1 weeks agoData Scientist, Lab & Protein Data
at Adaptyvbio
Lausanne, United StatesOn-site
Responsibilities
- - Build anomaly detection and QC models that catch bad data the eye would miss: assay drift, instrument variability, plate effects, false passes and false fails — and distinguish real signal from noise statistically.
Requirements
- Adaptyv is building an automated lab thats let AI agents run biology experiments.
- We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results.
- We're building the infrastructure that gives AI agents access to the physical world.
- We are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and the techbio companies pushing the field forward.
- We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation.
- This sits at the intersection of three things: data quality (is this number real, or an artifact?), bioinformatics (linking experimental results back to sequence, structure, and protein design), and dataset building (turning foundry output into the kind of high-quality, benchmarkable data that frontier AI labs actually want).
- - Work with the software and ML teams to specify, review, and improve the automated data pipelines that process instrument outputs, feeding back precise
- WHAT WE'RE LOOKING FOR - Strong data science / bioinformatics background — you're fluent in Python (pandas, numpy, the scientific stack) and comfortable owning messy, real-world experimental data end to end. - Genuine biology grounding — you understand proteins, assays, and sequence/structure/function well enough to know what the data means, not just how to process it.
- You move fast, systematize what works, and have no patience for babysitting a fixed dashboard. - AI-native builder.
- experience with protein/sequence-structure data (bioinformatics tooling, structural data), ML on experimental data, or building datasets for model training and benchmarking.
Benefits
- You're energized working across the lab bench, software, and ML, and you treat automation and data infrastructure as part of your job. - Bonus: