Data Scientist, Computational Toxicology

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Genentech, a member of the Roche Group!

The department of Translational Safety is looking for a computational toxicology data scientist to provide scientific leadership and contribute to the comprehensive and integrated safety profiling of Genentech drug candidates. The successful candidate will work closely with stakeholders across the company to analyze diverse data and develop novel computational tools that support safety-related decisions. Computational toxicologists in Translational Safety integrate state-of-the-art analytical methods to support molecular design, develop data to insight pipelines, and help guide strategic data generation to advance and validate New Approach Methodology (NAM) models for safety screening and mechanistic toxicity investigations.

Our scientists continuously look for opportunities to advance drug discovery and development and improve patient care through integration of computational methods and robust data streams.

• You will be responsible for in-depth analyses of multifactorial data streams from new and established toxicity assays to support decision on preclinical candidate safety; the typical data streams include transcriptomic, proteomic, metabolomic, image data and tabular data from more conventional reporter toxicity assays.
• You will integrate large scale omic data sets in support of in vitro assay development, NAM validation, and project-specific investigations into toxicity mechanisms.
• You will curate diverse historical data to develop predictive AI/ML models aimed at profiling drug candidates throughout the Genentech pipeline as part of the lab-in-a-loop facilitated molecule design and evaluation cycle.
• You will identify, evaluate, and propose novel computational approaches to augment safety predictions and impact safety-related decisions throughout the drug-development pipeline.
• You will collect and curate clinical and preclinical in vivo data to support translational validation of NAMs.
• You will work with cross-functional teams including toxicologists, biologists, chemists, data & computer scientists to develop fit-for purpose computational tools and drive the adoption of in silico NAMs throughout the drug development pipeline.
• You will actively engage with academic groups at the forefront of AI/ML, toxicology, and informatics.
• You will publish high quality impactful scientific articles and present at conferences, business meetings, and academic institutions.

• Education:
  
  PhD in computational biology, computational toxicology, computational chemistry, biomedical data science, statistics, machine learning, biotechnology, or a related field with 0-5 years of experience.
• Omics Expertise:
  In-depth understanding of modern omics data and analytical pipelines, with an emphasis on single-cell and spatial transcriptomics.
• Toxicology Background:
  Scientific background in toxicology or closely related life science, with a proven record of curating and interpreting bioassay data.
• Safety & Screening:
  Preferred experience with safety screening pipelines, drug candidate de-risking, and a general understanding of 3D in vitro systems.
• Machine Learning:
  Clear understanding of contemporary ML concepts and demonstrated interest in applying them to life sciences problems.
• Programming
  
  Skills:
  
  Strong programming skills in R or Python for large-scale data management and machine learning.
• Data Infrastructure:
  Experience with cloud computing, database architecture, and SQL.
• Data Analysis:
  Practical understanding of data processing and statistics in biological sciences and a record of integrating data across sources.
• Advanced ML Experience (Preferred):
  Experience with image processing, pattern recognition, automated literature data extraction, and/or developing LLMs, knowledge graphs, or generative models.
• Communication & Leadership:
  Ability to communicate and collaborate across life and computational…