Staff Computational Biologist

Freenome

Completely RemoteFull TimeHealthcare & Telemedicine
Posted Today

Job description

Responsibilities

  • Serve as a key thought-leader on the Computational Science team, leading the analysis and interpretation of cancer's molecular signatures
  • Guide and contribute to the development of models that characterize biological changes associated with cancer
  • Execute rigorous computational analyses on data from molecular assays including WGS, WGBS, RNA-seq, and protein quantitation
  • Design novel statistical models for the evaluation and modeling of data types within the context of cancer biology
  • Identify research hypotheses and execute associated research projects with a skilled team
  • Solve complex analytical challenges related to cell-free circulating nucleic acids and proteins
  • Partner with molecular biologists to refine wet lab experiments and development scientists to turn models into products
  • Support the professional development of cross-functional computational biologists

Requirements

  • PhD or equivalent experience in computational biology, cancer biology, statistics, or bioinformatics
  • At least 8 years of post-PhD experience in biological discovery and product development, preferably in cancer or diagnostics
  • Deep expertise in cancer and molecular biology
  • Extensive experience with high-throughput technologies (Methyl-seq, ATAC-seq, RNA-seq, Hi-C, etc.)
  • Proficiency in Python (Numpy, Matplotlib, Pandas) and modeling packages (Scikit-learn, TensorFlow, PyTorch) or R/C/C++

Preferred Qualifications

  • Experience training or applying biological sequence-to-function models
  • Experience with single-cell genomic or transcriptomic models
  • Experience modeling cell-free DNA (cfDNA) signals for early disease detection

About the Company

Freenome is dedicated to changing the entire landscape of cancer through advanced computational science and molecular diagnostics.

Skills & tools

PythonMachine Learning

What the team is looking for

Use this list as a quick fit check before you apply.

  1. 01PhD in quantitative field
  2. 028+ years post-PhD experience
  3. 03Expertise in cancer biology
  4. 04Proficiency in Python or R
  5. 05Experience with high-throughput genomics
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