Computational/Bioinformatics Scientist

Stealth Co • San Diego, CA

3h • Onsite

About The Position

We are building a high-throughput data analysis pipeline from first principles, on a short timeline, with a small team. This role is the connective tissue between biology and algorithm. You will support chemistry and assay teams with rigorous quantitative analysis, build first-principles simulations that let us study instrument behavior before reliable data exists, and own the downstream data processing stack. This is not a research position. You will write production-intent code, ship working tools, and own specific technical tracks end-to-end. The team is small, the problems are novel, and the timeline is real.

Requirements

• MS and 13+ years or PhD and 10+ years of experience in computational biology, bioinformatics, engineering, statistics, physics, computer science, or a closely related field; MSc considered with a strong industry track record in product-facing roles
• Must demonstrated experience shipping quantitative analysis tools or pipelines in a product development context — you have owned a technical track, shipped something, and moved it from prototype through to use
• Must have strong programming skills in Python (numpy, scipy, pandas/polars, scikit-learn) or R (tidyverse, Bioconductor); fluency in both is a plus
• Fluent working with multi-modal scientific data: tabular, image, and tensor formats — not just one
• Familiarity with high-through put data pipelines and computational biology and bioinformatics methods: biological data analysis, QC metrics, and quantitative modeling
• Prior direct experience interfacing with wet lab, chemistry, or assay teams — you know how to translate between biological experiment and quantitative model

Nice To Haves

• Experience at a life sciences instrument or measurement technology company.
• First-principles mechanistic simulation experience: PDEs, ODEs, stochastic systems, or optical/imaging physics — the ability to build a predictive model from governing equations before empirical data is available is directly useful here
• Demonstrated software projects: public GitHub contributions, open-source tools, or comparable evidence of engineering quality and technical ownership
• Comfort using AI-assisted development tools to move faster — this team expects everyone to use the best available tools
• Image analysis experience: microscopy, spatial data, or feature extraction from 2D/3D image stacks
• Experience with combinatorial design problems: code design, edit distance constraints, composition bounds
• Familiarity with on-instrument or embedded compute constraints (understanding what "runs in real time on hardware" means in practice)

Responsibilities

• Own the biology-facing analytics track: image-based signal extraction, sequential signal quality analysis, experiment design support, and quantitative troubleshooting for teams
• Build first-principles simulations of instrument behavior — signal distributions, optical models, and system dynamics — to study system behavior before reliable instrument data is available
• Design and implement the downstream data processing stack: signal diversity handling structured output formats
• Characterize data quality across difficult signal patterns and edge cases that stress classification and quality scoring models
• Define quantitative metrics and diagnostics to guide chemistry and hardware teams toward theoretically achievable performance limits
• Collaborate with the ML engineer on labeled datasets, ground truth construction, and calibration inputs for quality scoring and signal classification model development

What This Job Offers

• Job Type: Full-time
• Career Level: Senior