Quantitative Biologist

Arcadia Science•Emeryville, CA

13d•Onsite

About The Position

We are Arcadia Science, an evolutionary biology company founded and led by scientists. Our mission is to turn natural innovations into real-world solutions by developing systematic and quantitative approaches to leveraging biology for therapeutics R&D. We share our research as openly as possible to accelerate discovery and make our work broadly useful. The Opportunity We're closing the gap between biological data and biological understanding. Our Validation team does this by closing the design–build–test–learn loop through lab validation across diverse organisms. Read more about our work through our publications. We are seeking a Quantitative Biologist to join our Validation team with strong expertise in using computational analyses across modalities and biological scales to extract meaning from complex data, identifying the limits of existing analytical approaches, and developing new ones to address them. In this role, you will work shoulder-to-shoulder with experimental biologists, and bring your own firsthand experience at the bench to inform how data is collected, analyzed, and interpreted. You’ll work with imaging modalities (Raman spectroscopy, coherent Raman imaging, live-cell microscopy, quantitative phase imaging) and a range of other quantitative measurements across diverse organisms. Your contributions will be instrumental in validating existing tools at Arcadia, shaping how we design and interpret experiments, identifying potential assets, and building robust analytical infrastructure that scales across the team. Top candidates are rigorous, collaborative scientists who are equally at home writing computational pipelines and sitting down with a wet-lab colleague to think through a statistical analysis. They have strong intuitions about image data, high expectations for rigorous experimental design, and the communication skills to make quantitative reasoning accessible to scientists with diverse backgrounds. They have hands-on bench experience, generating biological data themselves and understanding the experimental realities that shape what analysis is even possible. Enthusiasm and independent documentation for participation in open science is also required as we routinely share our findings via our open-source pubs.

Requirements

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, as we expect all scientists to actively draft and openly publish their results

Responsibilities

Design and implement software for signal processing, image analysis, and quantitative interpretation of experimental data
Develop and maintain scalable workflows and pipelines that apply these methods to high-dimensional phenotypic data across diverse data types
Collaborate with experimental biologists to design statistically sound experiments before data is collected
Build and apply statistical and machine learning models to interpret complex biological datasets, including mixed-effects models, dimensionality reduction, and other approaches
Review and improve code written by scientists across the team, promoting reproducible and well-documented analytical practices
Develop SOPs and shared infrastructure (pipelines, notebooks, documentation) that help the broader team work more quantitatively and independently over time
Synthesize ideas, data, and findings into fully open-access pubs and engage with the scientific community to maximize impact and garner feedback that improves the work