Lead Computational Scientist, Drug Safety & AI Models

dimensioncap.com; South San Francisco (CA); Sur place; USD 150 000 - 190 000; Plein temps

Résumé du poste

Axiom in South San Francisco seeks a scientist to lead analysis of large, multimodal toxicity datasets to explain mechanisms of drug injury and improve predictions of human response. You will work with imaging, transcriptomics, proteomics, and bioassays to derive features for predictive models.

You will partner with ML scientists to ensure biological signals drive model improvements, design robust QC, and interpret results to inform safer drug design in collaboration with pharmaceutical partners.

Qualifications

• Experience in analyzing multimodal toxicology datasets.
• Ability to extract meaningful features from imaging, transcriptomic, and proteomic data.
• Strong statistical judgment and experimental design skills.
• Experience communicating scientific findings to cross-functional teams.

Responsabilités

• Lead the analysis of large, multimodal toxicity datasets to derive biological insights and features for predictive models.
• Characterize drug responses in primary human cells and multicellular systems, identifying injury-related phenotypes.
• Develop QC methods to assess reproducibility, batch effects, and assay performance across modalities.
• Collaborate with ML scientists to improve toxicity prediction models and validate biology-driven predictions.
• Investigate model behavior, interpret signals, and propose additional measurements to address gaps.
• Design protocols and assays with experimental teams to capture relevant biology and improve predictive performance.
• Partner with pharma/biotech teams to interpret toxicity profiles and guide drug development decisions.
• Help build AI systems that explain mechanisms and guide safer drug design.

Connaissances

Python programming

Statistics

High-content imaging

Machine learning

Outils

CellProfiler

Cellpose

napari

OpenCV

scikit-image

Description du poste

Axiom in South San Francisco seeks a scientist to lead analysis of large, multimodal toxicity datasets to explain mechanisms of drug injury and improve predictions of human response. You will work with imaging, transcriptomics, proteomics, and bioassays to derive features for predictive models.

You will partner with ML scientists to ensure biological signals drive model improvements, design robust QC, and interpret results to inform safer drug design in collaboration with pharmaceutical partners.