Revolution Medicines is a global, commercial-state oncology company dedicated to discovering, developing and delivering innovative medicines for patients with RAS-addicted cancers. Leveraging its differentiated RAS(ON) tri-complex inhibitor platform, the company is advancing a broad, integrated portfolio of oral RAS(ON) inhibitors designed to directly target the active, cancer-driving state of RAS. Founded on rigorous scientific inquiry and a willingness to challenge long-held assumptions, Revolution Medicines is committed to changing the trajectory of disease for patients with RAS-addicted cancers worldwide.

Our people are united by a shared way of working: follow the science, challenge assumptions, act with urgency and hold ourselves to a high standard of rigor—all in service of patients.

The Opportunity

In this role you will join the Cancer Genomics group in Clinical Biomarker Development, which sits within the Translational Medicine department to develop biomarkers, technology, and computational strategies for our RAS(ON) inhibitor programs. You will drive the application of advanced genomics analyses 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.

As a Cancer Genomics Senior Scientist I in Clinical Biomarker Development, you will:

Apply advanced analytics and genomics solutions to clinical biomarker data to inform research & development

Integrate multi-omics datasets to interrogate disease biology and prognosis, mechanisms of resistance, 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

Required Skills, Experience and Education

A Ph.D. in Cancer Genomics, Genetics, Computational Biology, Bioinformatics, or similar degree.

A minimum of 2 - 8 years post-PhD experience analyzing cancer genomics data (post-doc, biotech, pharma, diagnostics, or combination).

A strong foundation in cancer biology, preferably with an understanding of RAS/MAPK signaling pathways in pancreatic, lung, or colorectal cancer.

Proficiency programming in R and/or Python, and expert in clearly documenting work with a version control system (git).

Experienced in Linux and the cloud, including HPC clusters and command line interface, and developing genomics workflows for large scale NGS datasets.

Experienced analyzing ctDNA or other liquid biopsy data derived from blood samples.

Experienced in the interrogation of DNA sequence data derived from tumor tissue, such as gene-panel, whole-exome or whole-genome data and downstream analytics (clonality estimates, copy number variation, chromosomal instability, mutational signatures, etc).

Practiced in commonly used tools and methods for DNA data analysis (GATK, Bioconductor, etc) and in utilizing publicly available datasets (TCGA, cBioPortal) to interrogate cancer genomics data.

Strong understanding of statistics and the ability to generate robust predictive models with multidimensional 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.

Preferred Skills

Experienced analyzing clinical endpoints (ORR, PFS, OS) and/or experienced with clinico-genomic RWE analytics.

Longitudinal ctDNA data analysis for predicting clinical endpoints (ORR, PFS, OS).

Deep knowledge of pancreatic, lung, or colorectal cancer, preferably including how progression, response and resistance may be mediated by biomarkers detected in tissue and/or blood biomarkers.