Computational Scientist (Biology)

Axiom is building the translational intelligence layer for drug discovery: AI systems that help scientists predict human toxicity before drugs reach the clinic. We generate large-scale human-relevant biological data across primary human cells, multicellular tissue systems, high-content imaging, transcriptomics, proteomics, ADME, and functional assays. We've built the largest experimental-to-clinical dataset in the world.

We are looking for a computational scientist who can sit at the intersection of biology, data, and machine learning. You will analyze some of the world's largest multimodal toxicity datasets, discover biological signals that explain why molecules are safe or toxic, and help turn those discoveries into models used by leading pharma teams to make real drug development decisions.

Key Responsibilities

• Own exploration and analysis of massive multimodal toxicity datasets spanning high-content imaging, transcriptomics, proteomics, ADME, mass spec, and functional cellular readouts
• Identify biological signals that distinguish safe compounds from toxic compounds across liver, heart, kidney, and immune biology
• Turn noisy, high-dimensional experimental data into clear biological insights, robust features, and model-ready datasets
• Analyze high-content imaging and transcriptomic data from primary human hepatocytes and multicellular hepatic systems
• Conduct detailed model error analyses to understand what biology the models capture and where they fail
• Collaborate with ML researchers to improve models that predict human toxicity as a function of dose, exposure, Cmax, chemical structure, and biological response
• Work closely with wet lab scientists to shape new assays optimized for predictive modeling
• Partner with leading pharma and biotech teams to interpret molecule toxicity profiles

Technical Skills Valued

• Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks
• Statistical analysis, dose-response modeling, dimensionality reduction, clustering, classification, regression
• High-content imaging analysis, CellProfiler, Cellpose, napari, OpenCV, scikit-image
• Transcriptomics, proteomics, mass spectrometry, ADME datasets
• High-throughput screening, assay development, and experimental QC

Ideal Candidate

• A biologist who taught themselves to code, or a computational scientist obsessed with experimental data
• Exceptionally strong at both biology and computation
• Skilled at finding signal in messy biological data
• Obsessive about data quality, reproducibility, and scientific rigor
• Can bridge wet lab protocols and machine learning models