Tech Stack

Description

You will ensure the quality and consistency of complex biological datasets used to train AI foundation models. You'll develop validation protocols, standardize data, collaborate with R&D, and implement automated pipelines to maintain data integrity for biomedical breakthroughs.

Requirements

• MSc in Biology, Computational Biology, or Bioinformatics
• Deep understanding of transcriptomics data types and quality considerations
• Proven experience in data quality control and validation pipelines
• Proficiency in Python and data visualization libraries (e.g., matplotlib)
• Strong analytical and problem-solving skills

Responsibilities

• Develop and implement data validation protocols for histology, omics, and clinical datasets
• Design and automate data quality pipelines to identify issues early
• Establish and enforce data standardization practices for machine learning
• Collaborate with R&D team and external data providers on data quality
• Evaluate and validate external public data sources for training models