Computational Biologist, Cancer Biology About the Role As a Computational Biologist dedicated to Cancer Biology at Freenome, you will be key to the development of early, noninvasive tests for cancer detection. You will use a strong foundation in cancer biology to motivate hypotheses and drive improvements to our best-in class computational algorithms detecting molecular signatures of cancer. You will work closely with machine learning scientists, molecular biologists, and other computational biologists to drive the iteration of computational models while ultimately developing products which can be used in the clinic. How you’ll contribute: Motivate new hypotheses and computational experiments by remaining at the forefront of research developments in cancer biology, including but not limited to molecular signatures across various types and stages of cancer. Use foundational knowledge in cancer biology to analyze and interpret data from best in class molecular assays such as whole genome sequencing, whole genome bisulfite sequencing, targeted sequencing, RNA sequencing, and protein quantitation. Leverage, develop, and apply statistical tools for biological interpretation, such as pathway analyses, protein-protein interactions, and performing functional interpretation. Analyze and interpret features and characteristics of computational and statistical models in the context of cancer biology and progression. Overcome analytical challenges inherent in the study of cell-free circulating nucleic acids and proteins. Work closely with molecular biologists to collaboratively iterate on experiments in the wet lab, as well as with other computational biologists and machine learning scientists to improve computational models. What you’ll bring: PhD or equivalent experience in a relevant field such as biology, cancer biology, computational biology, bioinformatics, or equivalent. Extensive knowledge of cancer biology, and experience leveraging this knowledge for problems in cancer computational biology and diagnostics. Industry experience applying computational biology to biological discovery and product development. Experience in developing and applying statistical and/or machine learning algorithms. Expertise with biological and genomic data, tools, and public databases (e.g. ENCODE, TCGA, Blueprint, Cosmic). Fundamental understanding of the central dogma, including background in molecular biology, cancer biology, and familiarity with regulation of molecular processes. Experience in the analysis of high-throughput, quantitative technologies in genomics, epigenomics, proteomics, or transcriptomics (e.g. Hi-C, ATAC-seq, RNA-seq, MS). Strong quantitative reasoning and statistical analysis skills, with a demonstrated ability to apply them effectively to relevant scientific problems. Strong computational and programming skills, including thorough experience with Python statistical packages (Numpy, Matplotlib, Pandas). Equivalents in other languages like R or C/C++ are also suitable. Familiarity working in a Linux server-based environment. Excellent oral and written communication skills to communicate to both scientific and broader audiences. Ability to work on a cross-functional team in our highly collaborative environment, working with both computational and experimental scientists. About Freenome Freenome is on a mission to empower everyone with the tools they need to detect, treat, and ultimately prevent diseases.
