Founded by Priscilla Chan and Mark Zuckerberg in 2015, the Chan Zuckerberg Initiative (CZI) is a new kind of philanthropy that’s leveraging technology to help solve some of society’s toughest challenges – from eradicating disease, to improving education. Across our core initiatives of Science and Education, we’re pairing engineering with grantmaking, impact investing, policy work, and advocacy, to progress in our mission of building an inclusive, just and healthy future for everyone.
We believe we can help build a future for everyone.
- We aim to be daring, but humble: We look for bold ideas — regardless of structure and stage — and help them scale by pairing engineers with subject matter experts to build tools that accelerate the pace of social progress.
- We want to learn fast, but build for the long-term: We want to iterate fast and help bring new solutions to the table, but we also realize that important breakthroughs often take decades, or even centuries.
- Stay close to the real problems: We engage directly in the communities we serve because no one understands our society’s challenges like those who live them every day.
Our success is dependent on building teams that include people from different backgrounds and experiences who can challenge each other's assumptions with fresh perspectives. To that end, we look for a diverse pool of applicants including those from historically marginalized groups — women, people with disabilities, people of color, formerly incarcerated people, people who are lesbian, gay, bisexual, transgender, and/or gender nonconforming, first and second generation immigrants, veterans, and people from different socioeconomic backgrounds.
CZI Science's mission is to support science and technology that will help make it possible to cure, prevent, or manage all diseases by the end of the century. Interdisciplinary teams of physicians, biologists, computational scientists, and engineers can expand our understanding of the human body and illness — the science behind medicine. In the science initiative, CZI fosters collaboration between scientists and engineers, develops tools and technologies, and builds support for basic scientific research.
Single-Cell Biology is a program in CZI's Science Initiative's and advances its mission by accelerating the development and application of single cell technologies, which can be applied to characterize the molecular mechanisms of disease. We accomplish this by funding data generation to produce a reference of human cell types and then building visual tools that will enable scientists and physicians to compare and contrast the molecular functionality of diseased cells and tissues with their healthy counterparts. To ensure the reference reflects the cutting edge view of human biology, we are building a data platform that enables scientists to submit new data, and mechanisms to incorporate data into the reference.
Our team is seeking a Data Manager with experience in biological data formats, biological data analysis, and project leadership to join the Single-Cell Biology program, which currently supports over 200 grantees around the world. These groups are pushing the field forward and represent one of one of the most exciting and rapidly progressing fields in modern biomedical research. Your primary role will be to work with scientists, data curators, and scientific consortia to organize the creation of a high quality, standardized single-cell data resource. This will involve identifying key data generators, building and maintaining relationships, and assisting with data contribution. You will also help define the metadata we gather about datasets, the formats we use to store the data, and will help extend our platform to support novel data modalities. As a member of the Single-Cell Biology team, you will have a critical role executing our strategy to connect the scientific community and the data that they generate to tools and technology that supports their use.
- Own the team's “data roadmap” that tracks assay development and data generation in the field and among CZI grantees, with an eye towards ensuring data comes from diverse groups of representative research participants and data that will support the creation of integrated references of the cell types of tissues. This work is important to understand macro-trends in the field and ensure that our strategy supports current and future work in the field.
- Identify groups that we should engage with by immersing yourself intellectually in the scientific community, including reading the literature, attending conferences and establishing working relationships with leading external scientists.
- For each data modality that we prioritize, determine the data formats, metadata, and reuse patterns that best fit the needs of our computational biology user community.
- Have the opportunity to dig into the data by curating high value datasets, prioritizing cases where doing so can initiate collaborations leading to scientist-driven follow-up data contribution.
- Leverage insights from the scientific community to create consensus across the Single-Cell Biology team about the way novel data types should be represented, and how they should be annotated.
- Leverage interactions with grantees and other members of the scientific community who are actively generating data to Identify ways to improve the value of our data publishing products.
- Partner flexibly and responsively with other CZI Science staff — people such as program officers, product managers, software engineers, and user experience researchers — and work toward aligned goals to deliver datasets on the roadmap you will maintain.
- Contribute to a collaborative, inclusive, and intellectually rigorous team culture
- 5+ years of experience in roles related to computational or quantitative biology and familiarity with academic research. A PhD, or Master's degree plus work experience, is preferred.
- Significant experience in a discipline related to genomics or single-cell biology.
- Excellent written and verbal communication skills.
- Experience and desire to work with, translate between, and learn more about software and scientific teams.
- Experience leading team projects.
- Demonstrated ability to use at least one modern computational language used by the genomics community (R, Python)
- Exposure to the core mathematical and statistical techniques commonly used in biology is a plus (including classical statistics, applied bioinformatics, machine learning, etc.)