The Opportunity

State-of-the-art technologies that measure multiple cellular aspects of in vitro biology are at the heart of insitro's efforts to accelerate drug development. Computational biology is key to elucidating the relationship between these phenotypes and human disease and translating them into actionable outcomes.

We are looking for a computational biologist with expertise across diverse data modalities, including deep experience in either omics or imaging readouts, a strong understanding of cell and disease biology, and fluency with state-of-the-art analysis techniques. Your expertise will help the team navigate the complexities of identifying therapeutic targets from diverse data, elucidating biological mechanisms, and championing a culture of statistical rigor and experimental design to ensure our analyses meet the highest scientific standards.

In this role, you will directly impact target prioritization and drug development efforts, advance our understanding of diseases, and aid the development of new treatments. You will be part of a cross-functional team of life scientists, data scientists, bioengineers, software engineers, and machine learning scientists who strive to identify therapeutic targets and develop drugs of high efficacy and low toxicity. Based in South San Francisco, this position reports directly to the Head of Computational Biology and ML-Omics and offers an in-person hybrid schedule of three days per week.

You will be joining a vibrant biotech startup with 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

Multimodal Analysis & Target Discovery

Synthesize Multimodal Insights: Draw insights from multimodal analyses (microscopy, spatial proteomics, bulk/single-cell RNA-seq, human cohort data) to uncover disease mechanisms and generate therapeutic hypotheses

Identify Therapeutic Targets: Analyze diverse data from disease-relevant in vitro models to identify potential therapeutic targets from perturbation screens

Discover Biomarkers: Analyze data from diverse sources to identify and validate potential biomarkers for monitoring disease status and progression

Experimental Partnership

Partner on Experimental Design: Work with experimental biologists to design, troubleshoot, and optimize experiments that generate and validate mechanistic and therapeutic hypotheses

Provide Domain Expertise: Bring statistical and computational expertise to guide assay development and the biological interpretation of results

Analytical Rigor & Communication

Benchmark and Calibrate: Calibrate analysis tools and workflows, define performance metrics, and conduct benchmarking to select fit-for-purpose solutions

Communicate Findings: Share results with cross-functional stakeholders through reports, visualizations, presentations, and publications

About You

Experience & Qualifications

Education & Tenure: Ph.D. in computational biology, systems biology, bioengineering, computer science, machine learning, or a related discipline, with 3+ years of working experience post-graduation

Data Modality Depth: Hands-on experience with diverse data modalities, including at least one of the following: single-cell RNA-seq, fluorescence microscopy, spatial proteomics or transcriptomics, or label-free microscopy

Statistical Foundation: Deep understanding of statistical modeling and data analysis, with a demonstrated ability to rigorously interpret complex datasets and generate mechanistic hypotheses

Biological Grounding: An understanding of molecular biology or disease biology (e.g., neurological, cardiovascular, or metabolic disorders)

Programming Skills: Strong programming ability and proficiency with Python scientific packages such as NumPy and pandas

Publication Record: Meaningful contributions to high-quality work published in relevant computational biology, systems biology, life sciences, or biomedical venues