Computational Scientist

About the role

We are seeking a talented Computational Scientist to join our innovative team at Axiombio, where you will play a pivotal role in establishing the computational and biological framework for our toxicity prediction platform. This position will involve delving into extensive and varied toxicity datasets to uncover biological signals, enhance predictive models, and develop new assays. Your contributions will be vital in the creation of AI systems that elucidate mechanisms and inform drug design processes.

Key facts

What you'll do

• Spearhead the investigation and analysis of large-scale multimodal toxicity datasets, which encompass imaging, transcriptomics, proteomics, and functional cellular readouts.
• Identify subtle biological markers that can effectively differentiate between safe and toxic compounds across various human systems, including the liver, heart, kidneys, and immune system.
• Convert complex, high-dimensional experimental data into actionable biological insights, robust features, quality metrics, and datasets that are primed for modeling.
• Examine high-content imaging and transcriptomic data derived from primary human hepatocytes and multicellular hepatic systems, with a focus on phenotypes such as mitochondrial dysfunction and endoplasmic reticulum stress.
• Perform thorough model error analyses to evaluate model performance, pinpoint failures, and ascertain the need for additional data or new assays.
• Collaborate with machine learning experts to refine models that predict human toxicity based on factors such as dose, exposure, chemical structure, and biological response.
• Develop computational techniques for extracting significant signals from imaging, transcriptomic, proteomic, and biochemical assays.
• Design and enhance quality control systems for large-scale, high-throughput biological datasets.
• Work in close partnership with wet lab scientists to optimize new assays, ensuring both biological relevance and predictive accuracy.
• Collaborate with pharmaceutical and biotech teams to interpret toxicity profiles of molecules and clarify the biological underpinnings of model predictions.
• Contribute to the advancement of computational toxicology, developing AI systems that clarify mechanisms, reason over evidence, and support drug design initiatives.

Requirements

• A solid foundation in both biological sciences and computational methodologies.
• Proficiency in extracting meaningful signals from intricate biological data.
• Strong scientific judgment to differentiate authentic biological insights from noise or artifacts.
• A genuine interest in high-content imaging, transcriptomics, assay development, and the creation of extensive experimental-to-clinical datasets.
• A commitment to maintaining data quality, reproducibility, and scientific integrity.
• An understanding of the interplay between experimental design, assay biology, feature extraction, and modeling decisions.
• A willingness to engage with various biological systems and mechanisms, including hepatotoxicity, mitochondrial toxicity, and immune-mediated toxicity.
• A desire to address real-world scientific challenges rather than focusing solely on academic publications.
• An ambition to contribute to the foundational growth of a company from its early stages.

Nice to have

• Experience with machine learning frameworks and tools.
• Familiarity with bioinformatics and computational biology techniques.
• Knowledge of regulatory requirements in toxicology and drug development.
• Background in software development and version control systems.
• Experience in collaborating with cross-functional teams in a fast-paced environment.

Skills & tools

• Proficient in programming languages such as Python, with experience in libraries like Pandas, NumPy, SciPy, and scikit-learn.
• Skilled in statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation.
• Expertise in high-content imaging analysis, microscopy, morphology profiling, and image-based phenotyping.
• Familiarity with image analysis tools such as CellProfiler, Cellpose, napari, OpenCV, and scikit-image.
• Experience with high-dimensional biological datasets, including transcriptomics, proteomics, and mass spectrometry.
• Knowledge of high-throughput screening, assay development, automation, and experimental quality control.
• Ability to interpret biological implications of model outputs and error modes.
• Strong scientific communication skills, capable of translating complex analyses into clear and credible narratives.

Practical notes

This position is based in our San Francisco Global Headquarters and is a full-time role. While the compensation details are not specified, we offer a competitive salary and benefits package commensurate with experience. We are committed to fostering a diverse and inclusive work environment and encourage applications from all qualified individuals. If you are passionate about computational biology and eager to make a meaningful impact in the field of toxicology, we invite you to apply and join our dynamic team at Axiombio.