Senior Computational Biologist

Computational Biologist Opportunity At Gordian Biotechnology

Are you ready to own the analysis, predictive modeling and interpretation of large-scale perturbation single-cell transcriptomic screens, turning high-dimensional, complex gene expression state changes into clear biological conclusions that drive therapeutic decisions in cardio-renal-metabolic disease?

Large-scale transcriptomic data is at the heart of Gordian's discovery efforts, and the company will succeed based on our ability to accurately predict physiological changes in disease using single cell gene expression as a readout. In this role, you'll help ensure our screening and validation decisions are grounded in rigorous, transparent and state-of-the-art analyses, enabling us to confidently prioritize the best targets and interventions.

The Destination:

Gordian Biotechnology is a therapeutics company whose mission is to cure age-related disease and wake up every morning more capable than the day before.

Traditional ex vivo screening methods have failed to produce effective treatments, as age-related diseases have complex causes that include interactions with the aged environment.

To address this problem, Gordian's Mosaic Screening pools interventions in living animals, producing datasets with causal validation for hundreds of targets, in the living context of disease and mapped to human patients. This data lets us make the most informed choices on what new ideas for treating complex disease, and move validated targets into drug development.

We are running this discovery engine in successive indication areas, currently focused on cardiometabolic, to map the in vivo effects of every druggable target across every relevant organ. We then develop drugs and run clinical trials, both internally and in collaboration with multiple partners. And by pooling the data from each program, we can start to identify medicines with broad impact on the multimorbidity and decline caused by aging.

The Journey:

Our mission is audacious, and the path will be full of both challenges and excitement. Two things characterize the Gordian experience: 1) We work as a team, with ownership in our own roles and trust in each other. 2) We strive for extraordinary outcomes, and in doing so grow our skills and capability.

Team – Relying on each other begins with transparency. We set clear goals, visibly connecting individuals and teams to our company objectives. This empowers each of us to make autonomous decisions about our work, knowing how they will affect the bigger picture. Our communication happens out in the open. We give and receive feedback from a perspective of helping each other grow, share mistakes, and ask for help.

Extraordinary – Every day, we ask ourselves, "How could this process or outcome be even better?" Knowing our overall mission, we do what we think will make the most progress, without asking for permission. We don't shy away from big challenges or unknown territory, we find a way to excel.

Gordian is trying to accomplish something tremendously difficult, and that requires people who care deeply about doing exceptional work. We take ownership of our work, ask every day how we can push our science forward faster, and challenge ourselves, and each other, to continually raise the bar. Holding ourselves and each other to that standard has created an environment where each of us grows into a better version of ourselves.

Doing exceptional work also means building a team that can sustain it. We keep standing meetings to a minimum so people can focus on the science, encourage open collaboration through hands-on experimentation and "pre-mortem" discussions that strengthen experimental design, and make time to connect over weekly team lunches. We also encourage people to unplug with an unlimited vacation policy. The combination of mission-focus, high expectations, and enabling people to thrive has created an environment that talent finds rewarding, as evidenced by a voluntary attrition of only ~6%/yr.

If this environment sounds appealing, help us bring it to life. We are at an exciting inflection point: applying our technology to create comprehensive atlases of therapeutic targets across multiple diseases, partnering for financial and intellectual support, and translating these insights into new medicines. We want both your ability and personality along for the ride. Our culture is a source of great pride; it represents both who we are and who we wish to be.

About the Role:

Gordian is at an exciting inflection point, having recently generated in vivo perturbation data spanning over 500 targets across different in vivo contexts (Obesity, Heart Failure), and are actively expanding this to Chronic Kidney Disease (CKD). This growing resource is being utilized in collaboration with external partners looking to leverage our platform to help build the most comprehensive knowledge-graph of disease-relevant, translatable therapeutics spanning these disease areas. Your mission as a Computational Biologist at Gordian is to leverage our in vivo screens to decode how cells respond to genetic perturbation at the transcriptomic level, and translate those responses into predictions of physiologically-relevant, therapeutically-actionable outcomes in disease. Based on your work, Gordian will continue to push the frontier of extracting physiological predictions from cell states. You'll specifically focus on cardio-renal-metabolic indications and associated tissues (heart, kidney, adipose, liver, etc.), partnering closely with our 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 across the screen lifecycle — from experimental design and power calculations, to QC thresholds and dataset integration strategies, to the statistical frameworks used for hit calling and prioritization for validation. This includes developing and applying robust methods for modeling heterogeneous biological contexts (e.g., cell-type-specific perturbation responses, animal, batch or treatment context variability), identifying and correcting for confounders (e.g., cell cycle, ambient RNA, doublets, batch effects etc.), and selecting or designing appropriate positive and negative controls to validate effect sizes and method performance. You will communicate findings with rigorous attention to interpretability and generalizability — distinguishing robust, reproducible signal from context-specific artifacts — and ensure that QC metrics, model outputs, and troubleshooting insights flow back to the single-cell and experimental teams to iteratively improve assay design and data generation. You will also help define how we deploy agentic LLM systems to build modular, semi-automated frameworks for in-house QC, analysis, and interpretation — integrating cutting-edge computational methods (e.g., perturbation-response models, trajectory inference, representation learning) with clinically relevant genomic resources to address the specific biological question at hand.

Working in collaboration with our disease experts and in vivo team, you will help 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.

About You:

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 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.

You have a PhD in Bioinformatics, Computational Biology, or a related quantitative field, paired with deep domain expertise in disease biology — you're as comfortable discussing pathophysiology and experimental design with biologists as you are discussing model architecture with computational colleagues.

You have at least 2+ years of hands-on experience (post-graduate) analyzing single-cell transcriptomic data (industry and/or postdoctoral) across different biological contexts, with a proven track record of productivity (at least one peer-reviewed publication or pre-print with a major contribution as co-author relating to a computational method or adapted analysis framework applied to a specific disease-relevant system), and a genuine drive to develop or adapt novel analysis methods rather than rely solely on off-the-shelf pipelines.

You're proactive and excited about new developments in single-cell and functional genomics, human genetics, and computational modeling, and you actively bring promising new methods (e.g. foundation models, perturbation-prediction approaches, trajectory and state-transition modeling, etc.) into your analytical toolkit. You have concrete examples of building or substantially extending methods to answer a specific biological question,