Quantitative Biologist — Imaging & Data Science

Arcadia Science; Emeryville (CA); Vor Ort; USD 150.000 - 200.000; Vollzeit

Benefits dieser Stelle

On-site at Emeryville HQ

Competitive equity offering

Zusammenfassung

Arcadia Science in Emeryville, CA is seeking a Quantitative Biologist to join our Validation team. You will apply computational analyses across modalities and biological scales to extract insights from complex data, collaborating closely with wet-lab scientists to inform how data is collected and interpreted.

You will design and implement software for image analysis and signal processing, build scalable analysis pipelines, apply statistical and machine learning models, and contribute to

Qualifikationen

• Ph.D. or equivalent experience in biology, bioengineering, computational biology, or related field.
• Strong experience applying statistical design to biological datasets.
• Hands-on bench experience and ability to collaborate with wet-lab colleagues.

Aufgaben

• Design and implement software for signal processing, image analysis, and quantitative interpretation of experimental data.
• Develop 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 complex datasets.

Kenntnisse

Python

R

Image analysis

Statistical design

Machine learning

Biology knowledge

Written communication

Team collaboration

Ausbildung

Ph.D. or equivalent

Tools

Git

Jupyter

Docker

Jobbeschreibung

Arcadia Science in Emeryville, CA is seeking a Quantitative Biologist to join our Validation team. You will apply computational analyses across modalities and biological scales to extract insights from complex data, collaborating closely with wet-lab scientists to inform how data is collected and interpreted.

You will design and implement software for image analysis and signal processing, build scalable analysis pipelines, apply statistical and machine learning models, and contribute to