Freenome is seeking a Senior / Computational Biologist, Proteomics About the Role The Senior / Computational Biologist in Proteomics will be responsible for developing analyses and modeling strategies for early, noninvasive cancer detection. They will use a strong scientific foundation to develop and apply computational methods to proteomics datasets in order to discover biological signatures relevant to the early detection of cancer. 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. Responsibilities: Conduct research into measuring and understanding proteomic changes in blood and tissue during the early stages of cancer development. Execute on computational research projects to model protein dynamics that result from diseases such as cancer. Develop computational methods to overcome analytical challenges inherent to analyzing plasma proteomics datasets. Research and implement novel algorithms for analysis of large-scale proteomics datasets and improved label-free protein quantification.. Develop and maintain software pipelines for the processing, visualization, and analysis of proteomics data (e.g., mass spectrometry, immunoaffinity assays, etc.) Work closely with molecular and cancer biologists to collaboratively iterate on experiments in both the wet and dry lab. What We're Looking For: PhD or equivalent experience in a relevant, quantitative field such as biochemistry, biophysics, computational biology, computer science, systems biology, etc. 4+ years of industry experience working with proteomics datasets for biomarker discovery. Expert-level understanding of proteomic workflows for discovery and targeted proteomics. Experience in developing and implementing computational algorithms and pipelines for the processing of proteomics data. Hands-on experience working with LC-MS-based proteomic dataset (e.g., DDA, DIA, PTM identification) or other protein abundance detection methodologies (ELISA, microfluidic biosensors, fluorescence detection technologies). Experience with public databases and the ability to integrate them into in-house data analysis pipelines. Strong computational and programming skills, including thorough experience with Python statistical packages (Numpy, Matplotlib, Pandas) or equivalents in other languages. Excellent communication and teamwork skills. Nice to Haves: Familiarity with proteomic instrumentation, especially for DIA-based LC-MS/MS methodologies. Broad understanding of LC-MS based proteomics tools used for visualization and data interpretation, e..g., OpenMS, TPP, MaxQuant, MS-GF+, X!Tandem, Byonic, etc. Experience integrating proteomics and genomics datasets (e.g., proteogenomics). Experience combining open-source and/or commercially available software platforms for protein identification and annotation across multiple biosamples (e.g., cell lines and tissues). Experience in a collaborative software engineering environment, including the use of automated testing, version control, and deployment systems to reproduce and accelerate research. Experience using statistics and machine learning to provide analyses of complex mass spectrometry datasets to facilitate novel protein identification. Robust mathematical and statistical skills, and a track record of applying them to complex and noisy biological data. About Freenome Freenome is on a mission to empower everyone with the tools they need to detect, treat, and ultimately prevent diseases.
