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Nautilus Biotechnology is developing a single-molecule protein analysis platform designed to democratize access to proteomics research. As a Senior Bioinformatics Scientist, you will be a key member of the team translating raw proteomic data into reliable, actionable insights that drive assay development and optimization decisions.
You will partner closely with assay development, reagent, and platform scientists to design experiments, establish success criteria, and interpret results. Your work will span the full experimental lifecycle: understanding reagent lots, experimental design, raw data processing, statistical validation, and communicating findings to cross-functional teams. You'll independently analyze Iterative Mapping data end-to-end, from run metadata through image processing and protein decoding to statistical conclusions, then present clear recommendations to scientists and non-technical stakeholders alike.
Key responsibilities include optimizing assay performance by identifying levers for reproducibility and quantitative accuracy; guiding experimental design with appropriate controls, replicates, and sample sizes; conducting sensitivity and power analyses to set acceptance criteria before data collection; diagnosing unexpected results by forming and testing hypotheses across reagents, instruments, image processing, and algorithms; developing and maintaining quality control metrics and reports with defensible thresholds; accessing and integrating data from cloud warehouses, object storage, on-premises servers, and experiment metadata systems; converting recurring analyses into reusable, tested software tools; and working cross-functionally with chemists, engineers, and algorithm developers.
This role reports to a Bioinformatics Fellow and requires a minimum of three days in the office per week in San Carlos, CA.
REQUIREMENTS:
- PhD in Bioinformatics, Computational Biology, Biostatistics, Biophysics, Chemistry, or related quantitative field
- Minimum 4 years of industry experience
- Hands-on experience analyzing data from wet-lab experiments, ideally including assay development or optimization, with strong understanding of bench work and assay performance drivers
- Demonstrated ability to trace complex, real-world data problems from raw data to root cause
- Solid foundation in applied statistics: distributions, variability, hypothesis testing, power/sensitivity analysis
- Proficient in Python for data analysis and visualization (pandas, NumPy, SciPy)
- Comfortable working across local machines, shared servers, and cloud environments, including Linux and Git
- Exceptional written and verbal communication skills, including ability to explain technical findings to non-computational audiences
- Can-do attitude and desire to learn: eager to tackle unfamiliar problems, pick up new domains, and bring ideas forward
- Preferred: AWS data storage and processing services (Athena, S3), hands-on bench experience, proteomics experience, experience moving assays from early development through production, experience building dashboards or interactive tools for non-computational users (Dash, Marimo)
