Senior Computational Biologist, Oncology

Pathos•New York, NY

1d•Hybrid

About The Position

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.

Requirements

PhD (or equivalent industry experience) in computational biology, cancer genomics, bioinformatics, or a related field.
Direct experience supporting oncology drug development in a translational, biomarker, or early clinical (Phase 1/2) setting.
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.
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.

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.