Senior Manager, Computational Proteomics Research

Why join Freenome?

Freenome is a high-growth biotech company on a mission since 2014 to create tools that empower everyone to prevent, detect, and treat their disease. 

To achieve this mission, Freenome is developing next-generation blood tests to detect cancer in its earliest, most treatable stages using our multiomics platform and machine learning techniques. Our first blood test will detect early-stage colorectal cancer and advanced adenomas.

To fight the war on cancer, Freenome has raised more than $1.1B from leading investors including a16z, GV (formerly Google Ventures), T. Rowe Price, BainCapital, Perceptive Advisors, RA Capital Management, Roche, Kaiser Permanente Ventures, and the American Cancer Society’s BrightEdge Ventures. 

Are you ready for the fight? A ‘Freenomer’ is a mission-driven employee who is fueled by the opportunity to make a positive impact on patients' lives, who thrive in a culture of respect and cross collaboration, and whose work makes a significant impact on the company and their career. Freenomers are determined, patient-centric,  and outcomes-driven. We build teams around divergent expertise, allowing us to solve problems and ascertain opportunities in unique ways. We are dedicated to advancing healthcare, one breakthrough at a time.

About this opportunity:

As a Senior Manager, Computational Proteomics Research in the Computational Biology group at Freenome, 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 technical team leader with an outstanding record of scientific achievement both as a manager and individual contributor. 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 Freenome’s portfolio of diagnostic products.

What you’ll do:

• Lead and support the growth of the Computational Proteomics team in contributing to the development of Freenome’s proteomics discovery platform. 
• Contribute to the design of experiments at the forefront of protein characterization techniques including mass spectrometry data generation (including DDA, DIA, PTMs, AP-MS) and immunoassays and protein display technologies (ELISA, Olink, Luminex, MSD, flow cytometry).
• In collaboration with your team, design studies and execute analyses focused on discovering novel protein and peptide-based biomarkers.
• Develop new bioinformatics pipelines and computational tools to extract actionable information from high-throughput datasets.
• Partner cross-functionally with computational and wet-lab scientific leaders at Freenome to develop a multiomic scientific roadmap and research strategy.
• Inspire a culture of scientific innovation, focused on translating discoveries into high-impact clinical applications.

Must haves:

• Ph.D. in bioinformatics, cancer biology, or related field with a focus on computational proteomics.
• 5+ years of relevant industry experience, including 3+ years of experience leading and mentoring scientific teams. 
• Experience in experimental design and analysis of high-throughput, quantitative technologies in proteomics using Python and/or R.
• Experience in the evaluation and application of appropriate statistical methods given a hypothesis and available data.
• 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:

• 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.

Benefits and additional information:

The US target range of our base