Senior Computational Biologist

Description:

We are looking for an expert in omics data analysis, including deep understanding of cell biology and single cell transcriptomics, and fluent with state-of-the-art analysis techniques. Your expertise will help the team navigate the complexities developing disease relevant cell models and analyzing high throughput phenotypic screens, and ensure that the tools being developed are calibrated and effective, and that analyses are performed to the highest rigor and in line with best practices in the broader scientific community.

In this role, you will collaborate closely with experimental biologists and machine learning scientists, support the identification of novel phenotypes, the development of new screening paradigms, and advance our understanding of disease. You will utilize diverse machine learning and bioinformatic methods to perform diverse downstream analyses, including integrating with other data modalities, including imaging and human cohort data in order to extract insights about disease mechanisms. You will be part of a cross-functional team of life scientists, data scientists, bioengineers, software engineers, and machine learning scientists that strive to identify therapeutic targets and develop drugs of high efficacy and low toxicity. This role will be reporting to the Head of Computational Biology and ML-Omics . This is a hybrid position that requires you to be in our South San Francisco headquarters at least three days per week.

You will be joining a vibrant biotech startup that has many opportunities for significant impact. You will work closely with a highly talented team, learn a broad range of skills, and help shape insitro's culture, strategic direction, and outcomes. Join us, and help make a difference to patients!

Responsibilities
 

• Analyze and interpret omics datasets from disease-relevant in-vitro models to identify potential therapeutic targets from perturbation screens
• Partner with experimental biologists to design, troubleshoot, and optimize high-throughput experiments and workflows
• Integrate insights from multimodal data (bulk/single-cell RNA-seq, microscopy, spatial proteomics, human cohort data) to understand disease mechanisms and generate therapeutic hypotheses
• Calibrate analysis tools and workflows, define performance metrics, and conduct benchmarking to select fit-for-purpose solutions
• Communicate findings clearly to cross-functional stakeholders through reports, visualizations, presentations, and publications
   

About You
 

• Ph.D. in computational biology, systems biology, bioengineering, machine learning, or a related discipline, with 3+ years of working experience post graduation
• Extensive hands on experience analyzing single-cell and bulk RNA-seq data
• Experience working with functional genomic assays data (RNA/ATAC/ChIP-seq, etc)
• Experience analyzing data from perturbational screens (e.g. perturb-seq)
• An understanding of systems biology, molecular biology, or disease biology (e.g. neurological disorders, metabolic disorders)
• Experience with spatial proteomics and/or transcriptomics
• Strong programming skills and proficiency with Python scientific packages (i.e., numpy, pandas, scanpy)
• Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions
• Committed to writing well-commented code and documentation, and familiarity with coding best practices (i.e. version control, code review)
• Publication record of meaningful contributions to high-quality work in relevant computational biology, systems biology, life sciences, or biomedical venues