As Manager, Computational Proteomics Research in our Computational Biology group, you will play a key role in advancing the company's mission to detect cancer early via cutting-edge, non-invasive multiomics tests. You are a strong individual contributor with an outstanding record of scientific achievement, who has recently moved - or is ready to move - into a formal scientific leadership role. You will apply both your scientific expertise and leadership skills to mentor and grow a team that will work closely with Molecular Research scientists to design, execute, and analyze experiments identifying new protein and peptide biomarkers of disease. Via regular literature review and conference attendance, you and your team will keep abreast of the state of the art in computational proteomics and identify novel research avenues and opportunities. Finally, you will collaborate with a multi-disciplinary, multi-analyte scientific and engineering team to help translate the results of your work into our portfolio of diagnostic products. RESPONSIBILITIES
- Lead and support the growth of the Computational Proteomics team in contributing to the development of our proteomics discovery platform.
- Contribute to the design of experiments at the forefront of protein characterization techniques requiring mass spectrometry data generation (including DDA, DIA, PTMs, AP-MS) and immunoassays (ELISA, Olink, Luminex, MSD, flow cytometry).
- Design studies and execute analyses focused on discovering novel protein-based biomarkers.
- Develop new bioinformatics pipelines and computational tools to extract actionable information from high-throughput proteomics datasets.
- Partner cross-functionally with computational and our wet-lab scientific leaders to develop a multiomic scientific roadmap and research strategy.
- Inspire a culture of scientific innovation, focused on translating discoveries into high-impact clinical applications.
- Ph.D. in bioinformatics, cancer biology, or related field with a focus on computational proteomics.
- 3+ years of industry experience, including experience leading scientific teams
- Experience in experimental design and analysis of high-throughput, quantitative technologies in proteomics.
- Experience in the evaluation and application of appropriate statistical methods given a hypothesis and available data.
Expertise in effective data analysis and visualization using Python, R, or equivalent.
Experience with source code version control. Experience with cloud-based computing and containerized compute environments is a plus.
- Excellent oral and written communication skills to communicate to scientific and broader audiences, with keen attention to detail.
- Ability to work on a cross-functional team in our highly collaborative environment, working with both computational and experimental scientists.
- Valuable supplementary qualifications include:
- Industry experience applying computational techniques to biological discovery and product development.
- Knowledge of cancer biology, and experience leveraging this knowledge for problems in cancer computational biology and diagnostics.
- Experience using quantitative proteomics and systems biology approaches (e.g., proteogenomics, network analysis, gene ontology) for biomarker discovery or related applications.