Quantitative Biologist

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.

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

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

Benefits

• Benefits
• Competitive equity offering