Computational/Bioinformatics Scientist
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 toolsImage 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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
Number of Employees
11-50 employees
