Computational Scientist 3 – Cell & Tissue Genomics Full-time

Computational Biologist

We advance science so that we all have more time with the people we love.

The Department of Cell and Tissue Genomics (CTG) is focused on developing and applying state of the art genomics technologies (e.g. single cell omics, high-content imaging, perturbations, etc) to solve key challenges in disease biology, target discovery, and translational medicine. We are seeking a highly talented, creative, and motivated computational biologist to analyze cutting-edge high-dimensional profiling and screening data, with a particular focus on high-content imaging, to decipher complex cellular circuits. The candidate will collaborate closely with members of the department to extract biological insights from diverse high dimensional data sets and support the development of new technologies, leveraging existing computational workflows and developing novel ones.

The Opportunity:

As an integral member of our dynamic team, you will be:

• Analyzing high-content imaging data from novel technologies including optical pooled screens, spatial transcriptomics, and high-content perturbation screens, with the goal of extracting mechanistic insights into gene function and cellular phenotypes.
• Developing methods to analyze and interpret new types of perturbation data.
• Applying statistics and machine learning to investigate data and extract biological insights.
• Collaborating across diverse, cross-functional teams including experimentalists in CTG and the therapeutic areas and colleagues in the Computational Sciences Center of Excellence and the AI Biology & Translation departments

Who You Are:

• PhD in Computational Biology, Bioinformatics, Computer Science, or a related quantitative field. Alternatively, a PhD in molecular biology, immunology, bioengineering, etc. combined with a very strong record of data analysis, supported by publication in this area. Postdoctoral training is a plus.
• Strong publication record of biological discovery and/or methods development.
• Deep expertise in image analysis, including image processing, feature extraction, and morphological profiling. Familiarity with modern deep learning approaches (e.g., embeddings, foundation models) is a plus.
• Comfortable with the statistical principles behind current best practices in high-throughput data analysis.
• Fluent in Python and/or R.
• Excellent communication and presentation skills.
• Able to work successfully in cross-functional teams to collaboratively solve complex problems.
• Passionate about applying your analytical skills to understand health and disease.

Preferred Experience:

• Experience with in situ data (FISH or ISS barcode calling).
• Familiarity with image analysis frameworks or libraries (e.g., CellProfiler).
• Experience with genomic data including single cell RNA-seq and perturbation screens is a plus.
• Experience with applying machine learning to high throughput datasets is a plus.

Relocation benefits are available for this posting.

The expected salary range for this position based on the primary location of California for Senior Scientist is $124,300- $230,900 and Principal Scientist is $143,200-$265,900. 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.

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

If you have a disability and need an accommodation in relation to the online application process, please contact us.