Staff Computational Biologist

Freenome; United States; On-site; USD 188,275 - 270,375; Full time

Benefits offered by this job

Equity

Cash bonuses

Comprehensive benefits

Job summary

Freenome is seeking a seasoned Staff Computational Biologist to drive the Computational Science team, applying genomics, modeling, and statistics to identify cancer signatures and advance product-ready models.

You will collaborate with ML scientists and computational biologists to translate models into clinical-stage applications while leading project direction and mentoring colleagues. The role offers onsite, hybrid, or US-remote options from Brisbane, CA.

Qualifications

• PhD or equivalent in a quantitative field (computational biology, cancer biology, statistics, bioinformatics).
• 8+ years post-PhD applying computational techniques for biological discovery in industry settings (cancer/diagnostics preferred).
• Deep oncology and molecular biology expertise applied to computational biology problems.

Responsibilities

• Lead analysis and interpretation of cancer molecular signatures on the Computational Science team.
• Develop models to characterize biological changes in cancer and progression.
• Perform rigorous analyses on sequencing and proteomics data (WGS, RNA-seq, cfDNA, proteomics).
• Design novel statistical models for evaluating these data types in cancer biology contexts.

Skills

Python

Numpy

Pandas

Scikit-learn

TensorFlow

PyTorch

R

C/C++

Education

PhD in quantitative field

Job description

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 biology and statistics to drive the creation of new applications and enhance algorithms for identifying molecular signatures of cancer. You will collaborate closely with machine learning scientists and computational biologists to iteratively advance computational models and assays, playing a key role in bringing products to the clinic. By joining us, you will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer.

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.

What you’ll do:

• 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.
  

Must haves:

• 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++.
  

Nice to haves:

• 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.
  

Benefits and additional information:

The US target range of our base salary rate for new hires is $188,275 - $270,375. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered. Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page at freenome.com/job-openings/ for additional company information.

Freenome is proud to be an equal‑opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

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