FL69 Inc. is a privately held, early-stage biotechnology company with the vision to revolutionize the human therapeutic landscape. FL69 Inc. was founded by Flagship Pioneering, a team of entrepreneurial scientists that conceives, creates, resources, and grows first-in-category life sciences companies. Since 2000, Flagship has created over 75 groundbreaking companies that are pioneering novel and proprietary biological, industrial, and engineering approaches to solve major needs in human health and sustainability. These companies include Moderna Therapeutics (NASDAQ:MRNA), Seres Therapeutics (MCRB), Syros Pharmaceuticals (SYRS), Rubius Therapeutics (RUBY), Evelo Biosciences (EVLO), Kaleido Biosciences (KLDO), and Indigo Agriculture. FL69 Inc. is a highly dynamic, entrepreneurial, and innovation-driven organization seeking to hire an exceptional scientist to join our team.
FL69 is seeking a highly motivated and team-oriented candidate for a Senior Scientist, Proteogenomics. This individual will be responsible for the discovery and analysis of non-canonical proteins in large-scale proteomic datasets. The ideal candidate has a strong scientific foundation in proteomic data and a demonstrated ability to develop tailored biochemical and computational methods to analysis existing and novel datasets. The candidate will work closely with genomics scientists, computational biologists, and a functional genomics team to identify novel targets involved in disease and bring them to preclinical models.
- Analyze publicly available mass spec data at scale (including DIA & DDA)
- Develop an automated pipeline for new protein discovery
- Skilled in data interpretation: filter, parse and integrate proteomics data with other datasets at FL69
- Integrate proteomics with multiomics next generation sequencing datasets
- Experienced in statistical analysis of large-scale proteomic datasets
- Evaluate and implement new technologies and methodologies (biochemical and computational) that might enhance the discovery pipeline
- Conduct research into measuring and quantifying the association of proteins in disease models and relevant datasets
- Develop computational methods to overcome analytical challenges inherent for the detection of non-canonical proteins
- 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 cell biologists to collaboratively iterate on experiments in both the wet and dry lab.
- 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 novel protein identification and quantification
- Experience integrating proteomics and genomics datasets (e.g., proteogenomics).
- Expert-level understanding of LC-MS/MS proteomic workflows for discovery and targeted proteomics including DDA and DIA
- Expert-level in implementing novel software solutions to analyze publicly available datasets
- 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 developing and implementing computational algorithms and pipelines for the processing of proteomics data.
- Hands-on experience developing or applying HLA immunopeptidomics workflows to identify and quantify MHC bound peptides
- Capable of developing and implementing biochemical workflows to enrich for target protein groups
- 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.
- Familiarity with proteomic instrumentation, especially for DIA-based LC-MS/MS methodologies.
- Familiarity with top-down proteomics datasets
- Familiarity with Edman-based protein sequencing
- Broad understanding of LC-MS based proteomics tools used for visualization and data interpretation, e..g., OpenMS, TPP, MaxQuant, MS-GF+, X!Tandem, etc.
- 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.