Scientist I, Clinical Biomarker Development (Cancer Genomics)

Revolution Medicines • Redwood City, CA

59d • Hybrid

About The Position

Revolution Medicines is a clinical-stage precision oncology company focused on developing novel targeted therapies to inhibit frontier targets in RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) Inhibitors designed to suppress diverse oncogenic variants of RAS proteins, and RAS Companion Inhibitors for use in combination treatment strategies. As a new member of the Revolution Medicines team, you will join other outstanding Revolutionaries in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway. The Opportunity: In this role you will join the Cancer Genomics group within Clinical Biomarker Development, which sits in the Translational Medicine department to develop biomarker, technology, and computational strategies for our RAS(ON) inhibitor programs. You will drive the application of advanced genomics analyses & emerging NGS technologies to maximize insight generation from clinical samples in our Phase I-III studies. This role offers the chance to work in a collaborative and innovative environment, including on cross-functional teams with other computational scientists, biomarker leads, biostatisticians, and preclinical scientists.

Requirements

• A Ph.D. in Cancer Genomics, Genetics, Computational Biology, or similar degree in which cancer biology is integrated with genomics data analysis.
• Proficiency programming in R and/or Python for computational biology analysis.
• Expert in clearly documenting work with a version control system (git).
• Experienced in Linux and the cloud, including HPC clusters and command line interface.
• Practiced at developing genomics workflows for large scale NGS datasets.
• Expert in the interrogation of RNAseq data, such as bulk, single-cell, and spatial transcriptomics data, preferably including utilization of single-cell atlas data to inform analysis flow.
• Experienced working with DNA sequence data (mutation, liquid biopsy, CGP, WES, or similar).
• Practiced in commonly used tools and methods (Seurat, Scanpy, GATK, Bioconductor, etc) and in utilizing publicly available datasets (TCGA, cBioPortal) to interrogate cancer genomics data.
• Strong interpersonal, verbal and written communication skills; proactive, self-motivated, and adaptable in a dynamic environment.
• Demonstrated ability to translate complex data into clear biological insight, and to influence project direction through data-driven scientific inquiry.

Nice To Haves

• Experienced with AI/ML approaches to integrate single-cell & spatial transcriptomics data.
• Deep knowledge of pancreatic cancer, preferably including how progression, response and resistance may be mediated by tumor microenvironment and/or transcriptional programming.
• Trained in cancer signaling biology with strong understanding of RAS/MAPK signaling pathways.
• Strong understanding of statistics and the ability to generate robust predictive models for outcomes in multidimensional data.

Responsibilities

• Apply advanced analytics and genomics solutions to clinical biomarker data to inform research & development, including machine learning methods with high throughput NGS genomic data.
• Integrate multi-omics datasets (e.g. spatial transcriptomics, liquid biopsy, and other NGS approaches) to interrogate disease biology, resistance mechanisms, and predict drug response.
• Collaborate with cross functional teams, managing interactions with key stakeholders to deliver robust biomarker analyses and recommend follow up actions.
• Communicate results to expert and non-expert audiences, including internal cross-functional team members and external collaborators, through scientific publications and presentations.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree