Tech Stack

Description

You will serve as the primary computational partner for pipeline programs, analyzing complex biomarker, mechanistic, and real-world data to inform target, dose, indication, and combination strategies. Your work will span preclinical in vitro/in vivo studies and early-phase clinical trials, helping refine patient selection and deepen understanding of mechanism-of-action.

Requirements

• PhD in Computational Biology, Bioinformatics, Statistics, Epidemiology, or related field (or MS with significant industry experience) in oncology.
• 7+ years of relevant post-graduate experience in biotech/pharma with preclinical and clinical oncology datasets.
• Hands-on experience analyzing biomarker data (DNA sequencing, RNA-seq, ctDNA, tissue-based assays).
• Proficiency in Python or R, including data wrangling, statistical modeling, and reproducible research practices.
• Excellent communication skills to translate complex analyses into decision-ready narratives.
• Demonstrated use of AI tools in current role.

Responsibilities

• Design and execute analysis plans for biomarker data (bulk/scRNA-seq, DNA panels/WES, ctDNA, IHC/IF, multiplex protein assays) from preclinical models and clinical samples.
• Translate preclinical findings into clinical hypotheses by analyzing in vitro/in vivo data (e.g., Helicon screens, xenograft/PDX, pharmacology studies).
• Develop, validate, and operationalize predictive and pharmacodynamic biomarker signatures for trial design and patient stratification.
• Synthesize and communicate findings in a decision-oriented manner to cross-functional teams and governance.
• Contribute to internal decision-making, conference abstracts, and manuscripts.