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Staff Computational Biologist

๐Ÿ“… Jul 23
Computational BiologyGenomicsModelingCancer Biology

๐Ÿ“œ Description

  • Serve as a key thought-leader on the Computational Science team, analyzing and interpreting cancer's molecular signatures.
  • Guide the development of models that characterize biological changes associated with cancer.
  • Execute computational analyses on data from various molecular assays, including whole genome sequencing and RNA sequencing.
  • Design statistical models for evaluating and modeling data types in cancer biology.
  • Identify research hypotheses and execute associated research projects with a skilled team.
  • Collaborate with molecular biologists to refine wet lab experiments and turn research models into products.

๐Ÿ› ๏ธ Requirements

  • 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 modelsโ€”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.

โœจ Benefits

  • Family & Medical Leave Act (FMLA)

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