The Role

Gordian has generated in vivo perturbation data spanning over 500 targets across Obesity and Heart Failure, and is quickly expanding into Chronic Kidney Disease (CKD). As a Senior Computational Biologist, you will own the coordinated analysis of this vast resource across our cardio-renal-metabolic programs, spanning heart, kidney, adipose, and liver, taking each from raw data through to a prioritized set of testable biological hypotheses. You'll work directly with disease-area leads, bringing Perturb-seq or pooled perturbation-screen experience you already have to bear on our specific screen designs from day one, and you'll be expected to navigate ambiguity independently: when a dataset doesn't behave the way prior experience would predict, or when there's no obvious playbook for a given analytical decision, you're the one making the call and driving the project forward, using the data-driven evidence you bring forth.

That ownership spans the full analytical lifecycle: from adequately QC’ing our single cell data and assisting our Single Cell team make the most informed decisions to maintain reproducible and high quality standards, to streamlining the hit-calling protocol by leveraging state-of-the-art computational approaches, to taking confident hits and being involved in validation planning (best designs, useful assays to couple and validate screen predictions etc.). The emphasis of the role is on judgment and autonomy across concurrent and upcoming projects, not on execution of a fixed checklist. You'll bring in your past experience to in the context of a given screen, ground those decisions in real fluency with the underlying disease biology, and proactively surface open questions and trade-offs to your disease lead and computational teammates rather than working in isolation until you have a finished result.

In your first month, you'll become fluent in our in-house pipelines and workflows and independently propose analysis tasks across more than one of our Obesity and Heart Failure programs, starting with resource gathering and structured data exploration. By three months, you'll be owning your own set of projects end to end, making significant contributions to feature development and validation, and evaluating alternative analytical approaches with appropriate controls and statistical rigor. At six months, you'll help define strong positive and negative controls for these screens, partner directly and independently with disease experts on forward screen planning across multiple programs, and use existing validation comparisons to assess predictive power, proposing concrete improvements to analysis methodologies along the way.

About You

You must have a Ph.D. in Bioinformatics, Computational Biology, or a related quantitative field.

You have 2+ years of hands-on experience (can include post-doctoral experience, and in exceptional circumstances, restricted to graduate experience while attaining a Ph.D.) analyzing single-cell transcriptomic data, including direct, hands-on experience with Perturb-seq or pooled/barcode-driven perturbation-screening data; this is a requirement for the role, not a nice-to-have. You also have at least one peer-reviewed publication or preprint with a major contribution as co-author, relating to a single-cell focused computational method or adapted analysis framework applied to a disease-relevant system, with an associated code resource or package.

You have strong statistical foundations and can select appropriate statistical tests and modeling approaches based on the biological question and experimental design. You are comfortable with concepts including linear and generalized linear models, mixed-effects/hierarchical models, effect-size estimation, multiple-testing correction, permutation or resampling approaches, power considerations, and handling biological and technical sources of variation. You understand how cell-, sample-, donor-, animal-, guide-, and batch-level effects can influence inference in single-cell and perturbation datasets.

You are highly proficient in both Python and R for single-cell and general data analysis, including major ecosystems such as Scanpy/AnnData and Seurat/Bioconductor, and are comfortable moving between computational frameworks. You have experience evaluating or benchmarking alternative analytical methods and tools, rather than relying exclusively on a single established workflows and vignettes. Experience with regulatory-network, cell-cell communication, trajectory/state-transition, or multimodal single-cell analyses is particularly valuable.

You have working familiarity with NGS workflows, including technologies such as 10x Genomics and Cell Ranger, and common data formats including FASTQ, BAM, and feature-barcode matrices. You understand how sequencing, alignment, barcode/UMI processing, library quality, and data provenance can affect downstream single-cell analyses and can work effectively with data-infrastructure and experimental teams to troubleshoot issues when they arise.

You have a track record of success in high-agency work, consistently creating momentum rather than waiting for direction.

You want to do your best work alongside exceptional teammates and are energized by environments where people push each other to think more clearly, work at a higher standard, and grow into better versions of themselves.

You're comfortable owning multiple projects at once and operating with a high degree of autonomy in ambiguous situations, communicating your reasoning transparently to your collaborators and knowing when to make a call independently versus when to loop in a disease-area expert or teammate.

You truly want to play a key role in an early-stage startup screening new targets for intractable diseases of aging: A fast-paced environment full of both uncertainty and new challenges, demanding relentless resourcefulness.

Additional valuable skills if you have them

Prior work in cardio-renal-metabolic biology and relevant tissues (heart, kidney, adipose, liver), either directly or through close collaboration with disease experts.

Experience with in vivo Perturb-seq specifically, as opposed to in vitro or cell-line-only pooled screens.

Experience with spatial transcriptomics data (10X Xenium, Visium etc)

The Details:

Gordian aims to provide everything you need to thrive. Beyond our community and science, you’ll have enough equity to be a true stakeholder in the company, competitive salary, full health/dental/vision/life insurance, 401k with match, onsite lunch paid for 3 days a week, basic onsite gym, whatever vacation you need to be at your peak, and access to world-class mentors and advisors to support your professional growth. Our building is in the heart of the biotech capital of South San Francisco.