About the Job
At Freenome, we are seeking a seasoned Staff Computational Biologist to help grow the Freenome Computational Science team. The ideal candidate is an expert in computational biology, genomics, and modeling, with a proven track record in industry settings. In this role, you will apply your background in computational ...
The role reports to the Manager and Staff Computational Biologist leading genomics modeling within Computational Science. This position can be onsite, hybrid, or remote (US only), based out of our campus in Brisbane, California.
Serve as a key thought-leader on the Computational Science team, leading the analysis and interpretation of cancer's molecular signatures and staying current with the field.
Key Responsibilities
Serve as a key thought-leader on the Computational Science team, leading the analysis and interpretation of cancer's molecular signatures and staying current with the field.
Guide and contribute to the development of models that characterize biological changes associated with cancer.
Execute rigorous computational analyses on data from best-in-class molecular assays, including whole genome sequencing, whole genome bisulfite sequencing, targeted sequencing, RNA sequencing, and protein quantitation.
Design novel statistical models for the evaluation, characterization, and modeling of these data types within the context of cancer biology and progression.
Identify research hypotheses and potential areas for model improvement; plan, scope, and execute associated research projects with a skilled team of computational biologists.
Solve complex analytical challenges inherent to the study of cell-free circulating nucleic acids and proteins.
Partner closely with molecular biologists to collaboratively refine wet lab experiments, and with development scientists to turn research models into products.
Support the professional development and career growth of talented, cross-functional computational biologists within your team.
Required Skills & Abilities
PhD or equivalent experience in a relevant quantitative field (e.g., computational biology, cancer biology, statistics, bioinformatics).
At least 8 years of post-PhD experience applying computational techniques for biological discovery and product development, preferably in cancer or diagnostics within an industry setting.
Deep expertise in cancer and molecular biology, with a proven ability to leverage this knowledge for computational biology and diagnostics problems in cancer.
Extensive experience analyzing data and developing models for high-throughput, quantitative technologies in genomics, epigenomics, transcriptomics, proteomics, (e.g., Methyl-seq, ATAC-seq, RNA-seq, Hi-C, immunodetection assays).
Proficiency in computational and programming skills, including extensive experience with Python statistical packages (Numpy, Matplotlib, Pandas) and modeling packages (Scikit-learn, TensorFlow, PyTorch) or equivalents in languages like R or C/C++.
Experience training, evaluating, or applying biological sequence models—specifically sequence-to-function models that learn and predict functional biology directly from raw DNA, RNA, or protein sequences.
Experience developing or applying single-cell genomic or transcriptomic models to translate biological heterogeneity into generalizable regulatory or epigenomic signatures.
Experience modeling cell-free DNA (cfDNA) signals specifically for early disease detection and diagnostic applications.
