Scientist, Proteomic Bioinformatics

Flagship Pioneering, Inc.•Boston, MA

29d•$115,000 - $165,000

About The Position

ProFound Therapeutics is pioneering the discovery of the expanded human proteome to unlock a new universe of potential therapeutics. By integrating multi-omics, advanced computation, and translational biology, we aim to reveal and characterize thousands of previously uncharted proteins and systematically explore their role in health and disease. Summary We are seeking a highly skilled Proteomics Bioinformatician with deep expertise in computational data analysis for next-generation mass spectrometry technologies, quantitative proteomics pipelines and proteogenomics. The ideal candidate will develop, optimize, and implement workflows for processing large-scale proteomic datasets, and implement automated MS platforms.

Requirements

Master's w/ 4+ YOE or PhD in Bioinformatics, Computational Biology, Proteomics, Computer Science, or a related discipline.
Demonstrated expertise in mass spectrometry proteomics data analysis, with experience using tools such as FragPipe, DIA-NN, Spectronaut/Spectromine, Skyline.
Strong programming experience in Python and R for scientific computing, statistics, and visualization (e.g., NumPy, pandas, SciPy, scikit-learn, ggplot2, dplyr, tidyverse).
Hands-on experience with statistical analysis, differential expressions, FDR control, PTM analysis, and high-dimensional data processing.
Comfortable working with large, complex datasets and high-performance or cloud computing environments.
Familiarity with MS data formats, prediction models and conversion tools.
Excellent communication skills and ability to collaborate across computational and experimental teams.
Proven ability to write clean, reproducible, well-documented code.

Responsibilities

Develop, implement, and maintain bioinformatics pipelines for MS-based proteomics data (i.e. DDA, DIA, PRM, ion mobility separation)
Integrate and analyze large-scale datasets from platforms including Orbitrap instruments, timsTOF, and next-generation DIA.
Build pipelines for data QC, normalization, feature alignment, identification, quantification, and downstream statistical analysis.
Apply and develop statistical models, machine-learning methods, and visualization tools using Python and R.
Automate whole data processing workflows (e.g., Nextflow) and build reproducible analysis modules.
Collaborate with experimental teams to refine study design, troubleshoot MS data, and interpret results.
Produce comprehensive, well-documented code and reports for internal and external stakeholders.
Stay up to date with emerging proteomics tools, algorithms, and quantitative strategies.