Scientist II, Cancer Genomics, Clinical Biomarker Development
$132,000–$166,000 year
HybridRedwood City, California, United States
Job Summary
Apply advanced analytics and genomics solutions to clinical biomarker data from Phase I-III studies to inform research and development. Integrate multi-omics datasets to interrogate disease biology, prognosis, mechanisms of resistance, and predict drug response. Collaborate with cross-functional teams including computational scientists, biostatisticians, and preclinical researchers to deliver robust biomarker analyses and recommend follow-up actions. Communicate results to expert and non-expert audiences through scientific publications and presentations. This role sits within the Cancer Genomics group in Clinical Biomarker Development, driving technology and computational strategies for RAS(ON) inhibitor programs.
Required Qualifications
- A Ph.D. in Cancer Genomics, Genetics, Computational Biology, Bioinformatics, or similar degree
- A minimum of 2 - 6 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
- 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
Desired Qualifications
- Experienced analyzing clinical endpoints (ORR, PFS, OS) including survival analysis
- 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
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