Quantitative Biologist
Arcadia Science is an evolutionary biology company developing evolution-informed methods, tools, and computational models for therapeutics research and releasing open scientific resources.
Funding history
Investors
About Arcadia Science
Arcadia Science uses iterative computational prediction and experimental validation across target identification, research-organism selection, biologics design, and technology development. It also publishes open-source research tools, protocols, datasets, and applications for scientists.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design software for signal processing, image analysis, and quantitative interpretation of experimental data. You will build scalable pipelines for high-dimensional phenotypic data, collaborate on statistically sound experimental design, and apply statistical and machine-learning models to biological datasets. You will review code, develop shared analytical infrastructure and documentation, and publish open-access scientific findings.
Requirements
- Ph.D. or equivalent experience in a relevant biology, bioengineering, computational biology, biophysics, or cell biology field
- At least 3 years of relevant full-time scientific experience after a Ph.D. or equivalent
- Experience with statistically driven experimental design for biological datasets
- Experience collaborating with experimental scientists to design, analyze, and interpret studies
- Expertise developing software for biological image analysis
- Experience applying statistical or machine-learning methods to phenotypic biological data
- Proficiency in Python and/or R for data analysis, pipeline development, and code review
- Experience with instrument control, data-acquisition software, or hardware-to-analysis pipelines
- Hands-on bench experience generating biological data
- Verbal and written scientific communication skills
- Documented participation in open science
Responsibilities
- Design and implement software for signal processing, image analysis, and quantitative interpretation of experimental data
- Develop and maintain scalable workflows and pipelines for high-dimensional phenotypic data
- Collaborate with experimental biologists to design statistically sound experiments
- Build and apply statistical and machine-learning models to interpret biological datasets
- Review and improve code to promote reproducible and well-documented analytical practices
- Develop SOPs and shared infrastructure, including pipelines, notebooks, and documentation
- Synthesize findings into open-access publications and engage with the scientific community
Benefits
- Benefits
- Competitive equity offering
Hiring Process
Resume and application-question review, in-person interviews, short practical work test, reference and background checks.