We are seeking a Computational Biologist with strong Python skills to lead analyses across drug repurposing, drug combinations, biomarkers, precision medicine, and translational oncology. This role will work closely with biology, translational, clinical, and data science teams to identify therapeutic opportunities, define patient stratification strategies, and generate biomarker hypotheses for cancer therapies, including emerging areas such as HER2-low, resistance mechanisms, and priming strategies.
The ideal candidate combines strong computational and statistical skills with hands-on experience analyzing multi-omics datasets and translating biological insights into drug discovery and development decisions.
Key Responsibilities
Analyze and integrate bulk RNA-seq, single-cell RNA-seq, DNA sequencing, proteomics, and other omics datasets to identify biomarkers, mechanisms of response, and resistance pathways.
Support drug repurposing and combination strategy efforts through pathway analysis, perturbation data analysis, and drug response modeling.
Develop computational approaches for patient stratification, predictive biomarker discovery, and precision medicine hypotheses.
Apply statistical genetics methods such as GWAS interpretation, eQTL integration, colocalization, and related target validation approaches where relevant.
Analyze screening and perturbation datasets, including CRISPR, small-molecule, and combination studies.
Build and maintain reproducible analysis pipelines in Python for data processing, modeling, visualization, and reporting.
Collaborate with translational and disease-area scientists to prioritize targets, biomarkers, and therapeutic combinations.
Contribute to study design, data interpretation, and presentation of findings to project teams and leadership.
Support translational analyses tied to oncology programs, including receptor biology, tumor heterogeneity, and response/resistance in therapies such as ADC and HER2-low programs.
Required Qualifications
PhD, or MS with substantial industry experience, in Computational Biology, Bioinformatics, Systems Biology, Cancer Biology, Biostatistics, or a related field.
Strong programming skills in Python for scientific computing, data analysis, and workflow development.
Experience with RNA-seq analysis, especially differential expression, pathway analysis, and multi-sample comparisons.
Experience with single-cell data analysis or strong interest in building this capability.
Strong foundation in statistics for biomarker analysis, model development, and hypothesis testing.
Experience in drug discovery, translational research, oncology, or precision medicine.
Ability to work with cross-functional teams and communicate computational findings to non-computational stakeholders.
Preferred Qualifications
Experience with drug combination analysis and synergy scoring methods.
Experience with GWAS, eQTL, pQTL, or human genetics-driven target discovery.
Familiarity with survival analysis, clinical outcome modeling, or real-world/clinical datasets.
Knowledge of cancer signaling pathways, resistance biology, tumor microenvironment, and translational oncology.
Experience working in biotech or pharma drug discovery settings.
Familiarity with R, SQL, cloud workflows, and reproducible pipeline tools.
Keywords / Skill Areas
Computational biology, bioinformatics, precision medicine, biomarkers, RNA-seq, single-cell RNA-seq, GWAS, translational oncology, drug combinations, drug repurposing, systems biology, Python, multi-omics.