Senior Computational Biologist, Oncology

Senior Computational Biologist, Oncology

Drug development shouldn’t be guesswork, not when patients are waiting.

Pathos is building a next-generation biotech with AI at the core. Not as a feature, but as the operating system for how medicines get developed. We believe most drugs don’t fail because the science was wrong. 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?

Pathos exists to change that. 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.

This is not theoretical. We’re well-capitalized and have the leadership to build a generational company. We invest in and advance our own clinical-stage programs, using our AI platform to sharpen trial design, patient selection and biomarker strategy. So therapies reach the patients most likely to benefit, sooner.

If you’re driven by purpose, energized by complexity, and want to apply AI, biology, or both to redefine the future drug development, come build Pathos with us.

Pathos is building an AI-native biotech platform that turns real-world data and multimodal models into translational biomarker insights that directly shape drug development decisions—from early target validation through late-stage patient selection. 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. You will own and evolve genomics and biomarker pipelines that support our internal and in-licensed oncology assets, with a particular focus on mechanism-based biomarkers, response predictors, resistance biology, and patient stratification. 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.

You will generate and test biomarker hypotheses using deep genomic and multi-omic data derived from both external cohorts and Pathos-sponsored clinical trials, translating complex molecular signals into actionable insights for clinicians, program teams, and leadership. This role requires not just analytical excellence, but a strong understanding of how biomarkers are operationalized in real drug development settings.

If you want to apply cutting-edge computational and genomics methods to problems that directly determine how cancer drugs are developed and deployed, this is the place to do the most meaningful work of your career.

• 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.
• Generate and prioritize genomically grounded hypotheses tailored to different therapeutic modalities, including:
• Target dependency and pathway activation for small molecules
  Expression, heterogeneity, and spatial context for mAbs, bispecifics, and ADCs
  Biomarkers of payload sensitivity, internalization, and resistance for ADC programs
• Lead biomarker discovery and validation efforts across preclinical, translational, and clinical datasets, with a focus on:
• Predictive and pharmacodynamic biomarkers
• MOA and pathway activity signatures
• Resistance and escape mechanisms
• Analyze and interpret clinical trial biomarker data (Phase 1/2), linking molecular profiles to response, durability, safety, and survival endpoints.
• Partner closely with clinical, translational, and regulatory teams to:
• Define biomarker strategies for trial protocols
• 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.
• Contribute to cross-program biomarker standards, best practices, and reproducible analysis frameworks across the Pathos portfolio.