Data Scientist (Bioinformatics)

Location: Lausanne, Switzerland; on-site. Start date: ASAP.

Company

hirify.global is building an automated laboratory that enables AI agents to run biology experiments for biopharma, AI, and techbio customers.

What you will do

• Define scientific data-quality logic for binding, developability, biophysical, and functional assays, including signal ranges, control thresholds, failure modes, and edge cases.
• Build automated quality checks, anomaly-detection systems, and QC models to identify assay drift, instrument variability, plate effects, false passes, false fails, and noise.
• Work with software and ML teams to specify, review, and improve pipelines processing instrument outputs, including rules for flagging, rejecting, or routing data for human review.
• Connect experimental results with protein sequence, structure, and design data.
• Convert foundry output into structured, documented, benchmark-grade datasets for customers and protein-design model training and evaluation.
• Apply statistical analysis to thousands of samples across hundreds of simultaneous experiments and make results interpretable and comparable across runs.

Requirements

• Strong data science or bioinformatics background with fluency in Python, pandas, NumPy, and the scientific Python stack.
• Experience owning messy, real-world experimental data end to end.
• Genuine understanding of biology, including proteins, assays, and sequence/structure/function relationships.
• Statistical maturity in process control, anomaly detection, variability, and batch effects, with the ability to distinguish drift from noise and defend analytical decisions.
• Concrete evidence of building and deploying pipelines, tools, models, or datasets end to end for real-world use.
• Ability to build with coding agents such as Claude Code and critically assess their output.
• Ability and motivation to work across laboratory science, software, and ML, treating automation and data infrastructure as part of the role.

Nice to have

• Experience with protein or sequence-structure data, bioinformatics tooling, structural data, machine learning on experimental data, or datasets for model training and benchmarking.

Culture & Benefits

• Hands-on, non-management role focused on building production systems.
• Close collaboration with laboratory scientists, software and ML teams, and customers.
• Work on automated wet-lab infrastructure combining software, hardware, data pipelines, and AI systems.
• Full-time, on-site work in Lausanne with an ASAP start.