Quantitative Biologist
On-site · Emeryville, California, United States
Job Summary
Quantitative Biologist to join Arcadia Science’s Validation team. You will design and implement software for signal processing, image analysis, and quantitative interpretation of experimental data; develop and maintain scalable workflows applying these methods to high-dimensional phenotypic data; collaborate with experimental biologists to design statistically sound experiments before data are collected; build and apply statistical and machine learning models to interpret complex biological datasets; review and improve code written by scientists to promote reproducibility; develop SOPs and shared infrastructure (pipelines, notebooks, documentation) to help the broader team work more quantitatively and independently; and synthesize ideas and data into fully open-access pubs while engaging with the scientific community.
Required Qualifications
- Ph.D. or equivalent experience in biology, bioengineering, computational biology, biophysics, cell biology, or a related field
- At least 3 years of full-time relevant scientific experience post-PhD or equivalent
- Strong foundation in statistically driven experimental design with hands-on experience applying these to biological datasets
- Track record of working directly with experimental scientists to design, analyze, and interpret studies
- Demonstrated expertise developing software for biological image analysis
- Experience with statistical or machine learning methods applied to phenotypic biological data, including spectral or image data
- Proficiency in Python and/or R for data analysis, pipeline development, and code review
- Experience with instrument control, data acquisition software, or pipelines connecting hardware to analysis
- Direct bench experience generating biological data, with a working understanding of common sources of experimental variability and how they manifest in downstream analysis
- Excellent verbal and written science communication skills for both general and technical audiences
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