Data Scientist, Lab & Protein Data

Adaptyv; Vaud; On-site; CHF 120,000 - 180,000; Full time

Job summary

Adaptyv is building an automated lab where AI-driven agents design experiments and data pipelines transform raw measurements into clean, benchmark-grade datasets. This role focuses on the data quality and governance layer of the foundry, ensuring that data are accurate, comparable and ready for downstream AI model training.

This position partners with lab scientists, software engineers, and customers to define and implement automated QC, develop anomaly detection, and connect results back to

Qualifications

• Strong data science / bioinformatics background with Python (pandas, numpy).
• Genuine biology grounding with protein/sequence data understanding.
• Statistical maturity: process control, anomaly detection, batch effects.
• Prolific builder with end-to-end pipelines, datasets, and models.
• AI-native builder with experience using coding agents.
• Interdisciplinary by instinct across lab, software and ML.
• Bonus: experience with protein/sequence data or model training datasets.

Responsibilities

• Own the data quality logic across the foundry for each assay type and automate checks.
• Build anomaly detection and QC models to catch subtle data issues.
• Define data pipelines requirements with software/ML teams and guide data routing.
• Link experimental results to sequence, structure, and design to reinforce models.
• Turn foundry outputs into structured, benchmark-grade datasets for customers and model training.
• Apply statistics to multi-condition data at scale and ensure interpretability across runs.

Skills

Python (pandas, numpy)

Biology/data science background

Statistics / anomaly detection

Data quality mindset

AI-native builder

Education

MSc/PhD in bioinformatics / computational biology / data science

Tools

SQL

scikit-learn

TensorFlow/PyTorch

Pandas

Job description

Adaptyv is building an automated lab that lets 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. But they can't run the experiments themselves - that's still a manual, months-long process. 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. This is a rare chance to help advance some of the most important work happening in biotech today.

Our automated lab is powered by a deep software + hardware stack: lab instruments worth millions of USD reverse-engineered into API-controllable hardware, dozens of devices orchestrated through complex workflows, full observability on everything that happens in the lab, processing pipelines for messy physical-world data, and AI systems that troubleshoot production results and accelerate assay development.

We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation.

About the Role

You'll build out the data science layer of Adaptyv's foundry — the work that turns tens of thousands of raw, messy experimental readouts into clean, trustworthy, structured data that our customers, our models, and our own scientists can rely on. Binding (BLI/SPR), developability, biophysical, and functional assays all produce data at scale; your job is to make that data correct, comparable, and useful.

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). You'll work shoulder-to-shoulder with the lab scientists who run the assays, the software team who own the pipelines, and the customers who train models on what we produce. This is a hands-on build role, not a management one.

What You'll Do

• Own the scientific logic of data quality across the foundry: define what "good data" looks like for each assay type — expected signal ranges, control thresholds, failure modes, edge cases — and turn it into automated checks.

• 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.

• Work with the software and ML teams to specify, review, and improve the automated data pipelines that process instrument outputs, feeding back precise requirements for what to flag, auto-reject, or route to human review.

• Connect experimental results back to the protein side — sequence, structure, and design — so wet-lab data and computational models reinforce each other.

• Turn foundry output into structured, documented, benchmark-grade datasets that are a genuine asset for our customers and for training and evaluating protein-design models.

• Apply real statistical rigor to multi-condition data at scale — thousands of samples across hundreds of simultaneous experiments — and make the results interpretable and comparable across runs.

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 don't need to be a bench scientist, but you can't be biology-blind.

• Statistical maturity — process control, anomaly detection, handling variability and batch effects; you can tell drift from noise and defend the call.

• Prolific builder with the receipts to prove it. You've shipped a lot — pipelines, tools, models, datasets — and can point to concrete things you built end to end and put into real use, not prototypes that died in a notebook. You move fast, systematize what works, and have no patience for babysitting a fixed dashboard.

• AI-native builder. It's 2026 — you build with coding agents like Claude Code as a default, and you have sharp judgment about what they produce.

• Interdisciplinary by instinct. You're energized working across the lab bench, software, and ML, and you treat automation and data infrastructure as part of your job.

• Bonus: experience with protein/sequence-structure data (bioinformatics tooling, structural data), ML on experimental data, or building datasets for model training and benchmarking.

Details

• Location: Lausanne, Switzerland (on‑site)

• Type: Full time

• Start date: ASAP

Application deadline

We are reviewing applicants on a rolling basis.