Senior Computational Biologist

About the role

You will 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.

Gordian Biotechnology is a therapeutics company aiming 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 due to complex causes of age-related diseases, which include interactions with the aged environment. To address this, 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 enables us to make the most informed choices on new ideas for treating complex disease and move validated targets into drug development.

Responsibilities

• Leverage in vivo perturbation data 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.
• Guide key analytical decisions across the screen lifecycle, including experimental design, power calculations, QC thresholds, dataset integration strategies, and statistical frameworks for hit calling and prioritization for validation.
• Develop and apply robust methods for modeling heterogeneous biological contexts, identifying and correcting for confounders, and selecting or designing appropriate positive and negative controls to validate effect sizes and method performance.
• Communicate findings with rigorous attention to interpretability and generalizability, distinguishing robust, reproducible signal from context-specific artifacts, and ensuring 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.
• 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 with clinically relevant genomic resources to address the specific biological question at hand.
• Collaborate with disease experts and in vivo team 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.
• Establish and iterate on selection criteria for validation that improves screening efficiency and translatability across programs.

Requirements

• PhD in Bioinformatics, Computational Biology, or a related quantitative field, paired with deep domain expertise in disease biology.
• At least 2+ years of hands-on experience (post-graduate) analyzing single-cell transcriptomic data (industry and/or postdoctoral).
• 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).
• Drive to develop or adapt novel analysis methods rather than rely solely on off-the-shelf pipelines.
• Experience with single-cell analysis tools (e.g., Scanpy, Seurat) and general data analysis (Python, R).
• Strong statistical foundations and judgment around controls, confounders, and interpretability in single-cell data.
• Experience with NGS workflows and common file formats (FASTQ, BAM).
• Proactive and excited about new developments in single-cell and functional genomics, human genetics, and computational modeling.
• Excellent interdisciplinary collaboration skills, self-motivated, comfortable with ambiguity, and energized by close partnership with experimental teams and domain experts.

Qualifications

• Track record of success in high-agency work, consistently creating momentum rather than waiting for direction.
• Energized by environments where people push each other to think more clearly, work at a higher standard, and grow into better versions of themselves.
• Desire to play a key role in an early-stage startup screening new targets for intractable diseases of aging.
• Fast-paced environment full of both uncertainty and new challenges, demanding relentless resourcefulness.

Skills

• Deep understanding of disease biology and the ability to discuss pathophysiology and experimental design with biologists.
• Expertise in single-cell transcriptomics and the ability to discuss model architecture with computational colleagues.
• Experience with agentic LLM systems to automate or semi-automate workflows once established.
• Strong communication skills, able to convey results through honest, clear visualizations and concise summaries that resonate with both computational and experimental audiences.

Benefits

• Opportunity to contribute to groundbreaking research in aging and disease.
• Collaborative and supportive team environment.
• Challenging and rewarding work in a fast-paced startup setting.
• Competitive compensation package.
• Flexible work schedule and generous vacation policy.

Pay

Competitive salary commensurate with experience.

Schedule

Full-time, remote position.