Computational Biologist

Gordian Biotechnology•South San Francisco, CA

1d•Onsite

About The Position

Your mission as a Computational Biologist at Gordian is to measure the impact of single-cell perturbations on transcriptomic state, and use those signals to predict changes in physiological state under therapeutic conditions. You’ll focus on cardio-renal-metabolic indications and relevant tissues (heart, kidney, adipose, liver, etc.), partnering closely with disease-area experts and experimental teams to translate screen results into clear, testable biological hypotheses. Working collaboratively with other computational members, you will guide key analytical decisions that shape how screens are designed, QC’d, interpreted, and ultimately used to help select hits for validation. This includes developing and applying robust approaches for handling heterogeneous contexts, confounders, and controls, communicating conclusions with high interpretability and generalizability, and ensuring the right context and outputs flow back to the single-cell team and disease experts to continuously improve data generation and troubleshooting. You’ll also integrate internal screen data with relevant public genomics resources to connect perturbation-driven molecular changes to in vivo physiology, identifying high-confidence features that capture desirable phenotypes, and build a prioritized set of candidate targets for future screens on the basis of those predictions. Over time, you’ll help establish and iterate on selection criteria for validation that improves screening efficiency and translatability across programs. In your first month, you’ll become fluent in our in-house pipelines and workflows and independently propose analysis tasks supporting our Obesity and Heart Failure programs, starting with resource gathering and structured data exploration. By three months, you’ll make significant contributions to feature development and/or validation, including evaluating alternative analytical approaches with appropriate use of controls, statistical testing, and an emphasis on interpretability tied to mechanism-of-action validation. At six months, you’ll help define strong positive and negative controls (indicators) for these screens, partner independently with disease experts on forward screen planning, and use existing validation comparisons to assess predictive power, proposing concrete improvements to analysis methodologies along the way.

Requirements

PhD in Bioinformatics, Computational Biology, or a related quantitative field.
Recognized domain expert in computational biology with at least 2+ years of hands-on experience analyzing single-cell transcriptomic data (industry and/or postdoctoral) across different biological contexts, and a proven track record of productivity with at least one peer-reviewed publication or pre-print that includes a major contribution as a co-author
Proactive and passionate about assessing new developments in the field including and not limited to single cell and functional genomics, human genetics etc., using appropriate tools for analysis applications in each case.
Strong proficiency in both Python and R for single-cell analysis (for example, Scanpy and Seurat) and general data analysis, with the ability to move fluidly between ecosystems and experience working on high performance clusters (AWS, GCL)
Working knowledge of NGS workflows and common file formats (including FASTQ and BAM), with the ability to adapt or modify pipelines and communicate clear implementation requirements to infrastructure partners.
Strong statistical foundations and judgment around controls, confounders, and interpretability, plus the ability to communicate results clearly through honest, high-signal visualizations and concise summaries.
Applies in-depth knowledge to practical problem solving with a result-driven mindset
High ownership, self-motivation and strong cross-functional communication skills, with a track record of partnering effectively with experimental teams and domain experts in a fast-paced, ambiguous environment.

Nice To Haves

Experience with pooled perturbation and screening data (for example, CRISPR or barcode-driven screens) and single-cell perturbation analysis methods.
Prior work in cardio-renal-metabolic biology and relevant tissues (heart, kidney, adipose, liver), either directly or through close collaboration with disease experts.
Experience integrating vast public genomics resources with internal data and building scalable workflows for large datasets (HPC and/or cloud).
Experience with preclinical models for validation with functional readouts (e.g. in human explants, organoids, etc)