Staff Scientist—Computational Proteomics

Staff Scientist—Computational Proteomics

Broad Institute of MIT and Harvard

The Proteomics Group at the Broad Institute ( Steven Carr, PI ) is looking for a highly motivated and skilled computational proteomics scientist . The position is flexible and title/salary is commensurate with the candidate’s experience.

The successful candidate will focus on developing and applying innovative computational approaches to analyzing proteogenomics data spanning genomics, transcriptomics, proteomics, phosphoproteomics and other posttranslational modifications from cancer, cardiovascular, infectious and other diseases being actively studied in our group at Broad. The work will involve analysis of large-scale multi-omics data via pathway and network analysis, integrated with statistical and machine learning approaches, to derive biological insights and identify therapeutic opportunities.

This is a rare opportunity to be part of an interdisciplinary team of proteomics and computational scientists, biologists, and clinicians. The candidate will apply computational, statistical and machine learning methods to advance the state of the art in proteomics; develop data analysis strategies, write algorithms, and deploy computational tools for the exploration of large proteogenomics data sets; conceive, implement and test statistical models; work with wet-lab researchers to translate these models into testable experiments; analyze the data produced from these experiments; test and develop novel tools for pathway and network analysis with emphasis on integrating diverse omics data types; and implement algorithms as software for distribution to the global research community.

As part of the computational proteomics team , the successful candidate will interact with world-class researchers across our vibrant Broad Institute community as well as with scientists we are collaborating with at leading academic research centers around the world. The successful candidate will also contribute to several large-scale NIH-funded consortia we are part of including the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium and the Molecular Transducers of Physical Activity Consortium. Broad proteomics scientists have spearheaded numerous flagship publications in high profile journals stemming from these consortia.

Requirements:

• Ph.D. in Computer Science, Bioinformatics, Computational Biology, Statistics or a related quantitative discipline with at least 3 years experience working on real-world data. We are specifically looking for a talented and motivated researcher with a proven track record in applying computational methods to the analysis of large-scale ‘omics datasets.
• A strong background in statistics and machine learning with both breadth of knowledge (hypothesis testing, linear models, supervised and unsupervised learning methods) and depth in a specific area (e.g., pathway and network analysis, Bayesian analysis, probabilistic methods, deep learning).
• Proven programming skills with the ability to learn new languages and environments quickly. The group primarily uses R and Python along with shell scripts and other languages and technologies as needed, in a cluster and cloud computing environment.
• Exposure to mass spectrometry-based proteomics and/or computational proteomics is a strong plus.
• Excellent ability to learn quickly with creative problem-solving skills, along with effective communication, are essential to successful performance in the fast-changing research computing environment at the Broad Institute.

Working in the Broad’s Proteomics group provides the potential for your contributions to be utilized and recognized across a global network of researchers in mass spectrometry-based proteomics and proteogenomics.

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All Broad employees, regardless of work location, must be fully vaccinated for COVID-19 by Tuesday, October 12, 2021. Requests for exemption for medical or sincerely held religious beliefs will be considered.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.