Pathos AI

Senior Computational Biologist, Oncology

New York City, NY · Senior

Sponsorship not specifiedDetected 193 days ago

PythonData ScienceAgentic AIClinical TrialsBioinformaticsAnimal StudiesLeadershipCollaboration

About the role

We are redefining how oncology drug development is done: integrated, data-driven, and built from first principles.
As a Senior Computational Biologist, you will sit at the intersection of genomics, translational science, and clinical development.
Your work will span discovery through Phase 1/2 clinical trials, with direct impact on indication selection, dose expansion strategy, and Go/No-Go decisions.

Design, build, and maintain end-to-end translational genomics pipelines supporting oncology drug programs, including RNA-seq, DNA (SNV/CNV/structural variants), and multi-omic integration.
Partner closely with clinical, translational, and regulatory teams to: Define biomarker strategies for trial protocols Support dose escalation/expansion decisions Inform indication prioritization and patient enrichment strategies Translate multimodal model outputs and large-scale genomic analyses into clear, defensible recommendations for development teams and leadership.
Pathos is building a next-generation biotech with AI at the core.
We're building the largest foundation model in oncology and pairing it with proprietary AI systems, deep oncology expertise, and 200+ petabytes of multimodal data linked to patient outcomes, so we can make development decisions with more precision, much earlier.
We're well-capitalized and have the leadership to build a generational company.

5+ years' experience in a pharma or biotech company supporting oncology drug development in a translational, biomarker, or early clinical (Phase 1/2) setting.
Technical & Domain Expertise Deep expertise in cancer genomics and transcriptomics, including hands-on experience with RNA-seq and DNA variant analysis in clinical contexts.
Strong understanding of translational biomarkers across the drug development lifecycle, from hypothesis generation to clinical readouts.
Experience analyzing biomarker data from interventional clinical trials, including response modeling, survival analysis, and subgroup discovery.
Demonstrated ability to integrate genomic data with clinical endpoints and operational trial data.
Familiarity with large oncology datasets (e.g., Tempus, TCGA, AACR GENIE) and applying them to inform development strategy.
Who you are Training PhD (or equivalent industry experience) in computational biology, cancer genomics, bioinformatics, or a related field.

They fail because they were tested in the wrong patients, with the wrong assumptions, in trials that couldn't answer the real question: who benefits, and why?

We invest in and advance our own clinical-stage programs, using our AI platform to sharpen trial design, patient selection and biomarker strategy.

This listing is sourced directly from Pathos AI's careers page and normalized into a canonical job model.