Principal AI Scientist - Multimodal Translational Medicine

Key Skills

Aartificial intelligence, Bioinformatics, Data Analysis, Keras software library, Machine learning techniques, Python Programming, PyTorch, RNA-seq Data processing, TensorFlow

Job Description

We seek a highly motivated AI Scientist within Oncology Biomarker Development to drive translational medicine efforts focusing on Genentech's Oncology pipeline. The incumbent will apply advanced data analysis skills and machine learning in collaboration with a group of computational and biological scientists to perform translational oncology research around multimodal translational data sets for programs in clinical development. 

For this position, we are seeking a demonstrated scientific leader with a consistent and outstanding record of creativity and performance in multimodal data integration and analysis for cancer biology R&D, evidenced by peer reviewed publications in top tier journals, patent applications, and/or publicly available code repository.

As a Principal AI Scientist, you are expected to: 

• Lead advanced machine learning and analytical method application to extract disease biology insights and support innovative clinical development programs and publications on multimodal translational data sets that include single cell and spatial ‘omics, RNAseq, ctDNA, digital pathology, and clinical data.
• Actively and collaboratively promote scientific and technical innovation with other members of the Department.
• Direct and support publications in high quality scientific, technical or medical journals.
• Represent the department and translational teams externally through interactions with our key computational collaborators.
• Interact with key partners and investigators and effectively manage internal and external scientific collaborations.
• Participate and thrive in an interactive, collaborative, and team-oriented culture.

Who You Are 

• PhD degree in a relevant field (e.g. computer science, computational biology, bioinformatics, molecular biology, cancer biology, genomics).
• Minimum of 3 years post-PhD experience, with demonstrated scientific leadership in integrating, analyzing, and interpreting multimodal biological data in an academic and/or industry setting.
• Fluency with Python and data analysis using modern frameworks for deep learning (e.g. Pytorch, TensorFlow-Keras).
• Experience with RNAseq data, systems biology tools, and pathway analysis.
• Excellent communication and collaboration skills.
• Experience with image analysis methods, particularly around spatial biology, is desirable.

The expected salary range for this position based on the primary location of South San Francisco, CA is $165,600- $307,500. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits

#gCS

Who we are

A member of the Roche Group, Genentech has been at the forefront of the biotechnology industry for more than 40 years, using human genetic information to develop novel medicines for serious and life-threatening diseases. Genentech has multiple therapies on the market for cancer & other serious illnesses. Please take this opportunity to learn about Genentech where we believe that our employees are our most important asset & are dedicated to remaining a great place to work.

Diversity and Inclusion (D&I) are critical to the success of our company and our impact on society. We believe that by championing diversity of background, thought and experience, we can foster a sense of belonging and provide an environment where every employee feels valued, included, and able to contribute their best for the patients we serve. We’re focused on attracting, retaining, developing and advancing our people to their full potential by rewarding bold ways of thinking and integrating inclusive behaviors into every aspect of our work.