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.

How We Build
Pathos does not operate like a traditional biotech. There is no middle management. There are no layers of approval. The company is designed, from the ground up, around small teams of 2–4 subject-matter experts who each command hundreds of AI agents to do the work that used to require dozens of people.

Everyone builds. Everyone ships. Every function at Pathos — from clinical execution to asset selection to the foundation model itself — runs on this model. Our product velocity delivers meaningful outcomes in hours instead of weeks. This is not a future aspiration. It is how we operate today.

The people who thrive here are operators: deep experts who can specify what needs to happen, orchestrate AI agents to execute at scale, and make high-judgment calls that compound over time. If you have spent your career building and shipping AI systems at scale, this is the environment where that experience becomes a superpower.

About the Role

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.

Key Responsibilities

• 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
  • 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.
• Contribute to cross-program biomarker standards, best practices, and reproducible analysis frameworks across the Pathos portfolio.

Who you are: 

Training

• PhD (or equivalent industry experience) in computational biology, cancer genomics, bioinformatics, or a related field.
• 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.
• Experience generating and validating genomic and multi-omic hypotheses across multiple drug modalities, including small molecules, monoclonal antibodies, bispecifics, and ADCs, with an understanding of how mechanism of action shapes biomarker strategy.
• Strong intuition for modality-specific biomarker requirements, such as target expression thresholds, pathway addiction, synthetic lethality, immune context, and resistance biology.
• 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.
• Fluency in R (and/or Python) with a strong emphasis on reproducibility and scientific rigor.

Mindset & Collaboration

• Thinks like a drug developer, not just a data scientist—understands what makes a biomarker actionable in a real clinical program.
• Comfortable owning analyses that influence high-stakes decisions, including trial design and program prioritization.
• Excels at cross-functional collaboration with clinicians, translational scientists, and engineers.
• Able to clearly communicate complex genomic and biomarker findings to both technical and non-technical stakeholders.

Location

This is a hybrid role, requiring up to 3 days per week onsite, in our NYC Headquarters.