The Senior Computational Biologist will be responsible for leading the scientific direction and execution for the development of early, noninvasive detection tests for multiple cancers. They will use a strong biological and scientific foundation in cancer genomics to develop best in class assays to capture novel biology, and then using various analytical approaches to discover specific biological signatures relevant to the early detection of disease. They will work with machine learning scientists, molecular biologists, engineers and other computational biologists to drive the iteration of research experiments while ultimately developing products which can be used in the clinic. A passion for innovation and demonstrated initiative in tackling new areas of research Responsibilities: Lead the development and analysis of best in class molecular assays such as whole genome sequencing, whole genome bisulfite sequencing, targeted sequencing, RNA sequencing, and protein quantitation. Lead research projects to model various biological changes resulting from diseases such as cancer, autoimmune disease, and infection. Lead genomic projects for early cancer detection by discovering signal in cfDNA, which can be composed of ctDNA and immune derived nucleic acids. Lead the research into understanding the changes seen within the blood during the early stages of cancer development. Overcome analytical challenges inherent in the study of cell-free circulating nucleic acids and proteins. Work closely with molecular and cancer biologists to push the iteration of various hypotheses in both the wet and dry lab. What We're Looking For: PhD or equivalent experience in a relevant, quantitative field such as computational biology, bioinformatics, cancer biology, computer science, etc. Fundamental understanding of the central dogma, including background in molecular biology and familiarity with regulation of molecular processes. Experience in the development and analysis of high-throughput, quantitative technologies in genomics, epigenomics, proteomics, and transcriptomics (e.g. Hi-C, ATAC-seq, RNA-seq, MS). Robust mathematical and statistical skills, and a track record of applying them to complex and noisy biological data. 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 communication and teamwork skills to take advantage of our highly collaborative environment, working with both computational and experimental scientists. Nice to Haves: Industry experience in applying computational biology for biological discovery and product development. Extensive knowledge of the molecular signatures of cancer. Expertise with biological and genomic data, tools, and public databases (e.g. ENCODE, TCGA, Blueprint, Cosmic). Experience in a collaborative software engineering environment, including the use of automated testing, version control, and deployment systems to reproduce and accelerate research About Freenome Freenome is on a mission to empower everyone with the tools they need to detect, treat, and ultimately prevent diseases.
