May 19, 2021
South San Francisco, CA
As a Director of Computational Biology, Assay R&D at Freenome, you will be a leader contributing to the development of noninvasive tests for early cancer detection. You will lead a team of talented computational biologists and statistical scientists working closely with wet-lab scientists in Freenome’s Molecular Research and Assay Development groups to drive the conception, design, optimization, and development of high-throughput assays for cell-free nucleic acids, circulating proteins, and other biomarkers in the blood. This highly collaborative work will produce the assays that are integrated into Freenome’s multiomics biomarker discovery platform and commercial tests for early cancer detection. You will partner with leadership and scientific staff in the Molecular Research and Assay Development departments to plan studies, then support your team’s analysis and interpretation of experimental data. You will leverage a deep understanding of computational approaches to cancer genetics, epigenetics, and molecular biology to provide scientific inspiration and technical guidance to team members, helping them to grow and develop as independent scientists, and empowering them to do their best work. Finally, you will be a key member of Freenome’s scientific leadership team, contributing to decision making and prioritization of our research and development directions.
How you’ll contribute
Lead a team engaged in research and development projects to measure and model biomarkers associated with cancer and precancerous lesions. Team responsibilities include:
Applying knowledge of computational biology to support the development of assays for high-throughput measurement of cell-free nucleic acids, circulating proteins, and other biomarkers in the blood.
Understanding, constructively applying, and developing best-in-class computational tools to extract actionable information from high-throughput assay datasets.
Collaborating with wet-lab scientists to rapidly iterate and to design or improve on existing experimental methods and quality control metrics, by providing real-time assessments of performance throughout the assay R&D process.
Identifying, implementing, and characterizing the behavior of appropriate quality control metrics throughout the assay development process.
Providing computational and statistical support to bench scientists throughout Freenome’s Molecular Research and Assay Development teams.
Collaborating with bioinformatics and machine learning pipeline engineers to scale and strengthen computational approaches from the R&D lab through to deployment as high-throughput reproducible workflows, and ultimately as components of the regulated, production-grade software used by Freenome’s commercial blood tests.
Serve as a key thought leader on the Computational Science and larger Science leadership teams. Partner with other scientific staff at Freenome to develop a scientific roadmap and research strategy.
Nurture and grow a computational scientist team, by mentoring existing staff and by recruiting and hiring new staff with skill sets and scientific development goals aligned with Freenome’s needs and mission. Create opportunities for your team members to undertake independent work and shape their own scientific and professional directions.
Have a multiplicative effect by building and harmonizing data analysis infrastructure and best practices within the team, and aligning analysis and infrastructure practices with Freenome’s larger Computational Science and Software Engineering communities when appropriate.
Inspire a culture of scientific innovation, translating discoveries into high-impact clinical applications.
Model “servant leadership” by maximizing the full team’s potential for impactful contribution.
What you’ll bring
PhD or equivalent experience in a relevant, quantitative field such as computational biology, statistics, bioinformatics, or equivalent. Alternately, a PhD in molecular or cancer biology (or similar) with extensive evidence of high-quality application and mastery of contemporary computational biology techniques (e.g., lead authorship on peer-reviewed publications).
7+ years post-PhD experience applying computational techniques to biological discovery and/or product development. Record of high-quality achievement demonstrated by peer-reviewed publications, patents, or successfully developed products.
Industry experience working in a diagnostics, pharmaceutical, or other biotechnology environment. Experience with CLIA/NYS/FDA assay validation studies is a plus.
3+ years of demonstrated experience managing and mentoring computational scientists.
Extensive knowledge of cancer molecular biology, with experience leveraging this knowledge for problems in therapeutic or diagnostic research and development.
Outstanding command of statistics, quantitative data analysis, and complex data visualization, including deep experience with statistical packages in Python, R, or equivalent.
Deep experience in analyzing several of the following biological data modalities: genomics, epigenomics, proteomics, transcriptomics (RNA-seq).
Understanding of library preparation methods used to generate next-generation sequencing data, including whole-genome and targeted approaches, genetic and epigenetic DNA characterization, and RNA-seq.
Track record of selflessly supporting and growing highly effective cross-functional teams, and of collaborating closely with bench scientists. Direct experience developing novel molecular biology assays, which could possibly include hands-on experience at the bench, is a plus.