Tech Stack

Description

You will design and implement machine learning methods for cancer diagnostics, focusing on epigenomics and oncology. Collaborate with molecular biologists and contribute to scalable ML pipelines. This role involves developing, validating, and advancing state-of-the-art approaches for cancer detection and monitoring.

Requirements

• PhD in Computer Science, Machine Learning, Statistics, Bioinformatics, or related field
• 6+ years post-PhD experience with emphasis on cancer epi/genomics
• Deep expertise in core ML models (GLMs, kernel methods, tree-based, neural networks)
• Proficiency in Python and scientific computing (NumPy, Pandas, Scikit-learn)
• Strong cross-functional communication skills

Responsibilities

• Design, implement, and validate machine learning and statistical methods for cancer diagnostics
• Contribute to best practices in model interpretability, uncertainty estimation, and reproducibility
• Design robust feature engineering and extraction pipelines tailored to biological data
• Prototype and productionize models using MLflow, Airflow, and Docker
• Collaborate with molecular biologists on experimental design and data quality control