Principal Data Scientist, Machine Learning

CZ Imaging Institute

In our Science work, our 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. We build open source software tools to accelerate science and generate more accurate and biologically important sources of data, fund scientific research worldwide to advance the frontiers of knowledge, and launched a family of institutes to do research that can't be done in conventional environments.

To accomplish our mission, we need to develop a dynamic, integrated view of biological systems in health and disease. The Chan Zuckerberg Institute for Advanced Biological Imaging (CZ Imaging Institute) aims to push forward the development and application of new imaging technologies over the next 15 years to obtain deep insights into the architecture of complex biological systems at the molecular level. Researchers will collaboratively develop breakthrough biological imaging systems centered around grand challenges that push the boundaries of what we can see and measure.

As an extension of the Chan Zuckerberg Initiative's Imaging program, the CZ Imaging Institute will create breakthrough technologies — hardware, software, biological probes, data, and platforms — that will be made available to the scientific community and adopted worldwide through a combination of direct access to the institute, open sharing of advances, and commercial partnerships.

The Chan Zuckerberg Imaging Institute is seeking an experienced Principal Data Scientist, Machine Learning, to lead our Machine Learning R&D team focused on addressing the challenges of analyzing and interpreting electron microscopic data obtained of cells. These efforts are in support of the overall mission of the Chan Zuckerberg Imaging Institute which is to develop technologies and methodologies to reach a molecular understanding of cellular function.

What You'll Do

• Lead the design, implementation, and refinement of the methods and tools that will enable the research groups and technology teams to better understand and interpret complex biological data sets
• Lead the development of machine learning models and algorithms to extract insights from large-scale data sets, develop new computational methods for the analysis of biological data, and provide guidance on the implementation of machine learning in various research and technology projects
• Drive innovation in the field of machine learning and data analytics in the core R&D areas at CZII
• Lead a collaborative team that develops, deploys, & maintains innovative systems and tools that enable the analysis and interpretation of cryoET and related datasets
• Establish yourself as an international leader in the area of machine learning applied to electron microcopy imaging

What You'll Bring

• A PhD in computer science or related subject, with a focus on machine learning & data analytics for biological imaging
• Domain knowledge in cryo electron microscopy is highly desirable
• Ability to lead a team in a highly interactive and cross-functional collaborative environment with a diverse team of colleagues and partners
• Outstanding analytical and problem-solving skills
• Excellent communication and interpersonal skills
• An ability to multitask and work collaboratively and cooperatively with technology developers and scientific researchers
• The ability to stay current with new technology development and related research fields and identify critical areas for high impact innovation

The Redwood City, CA base pay range for this role is $214,000 - $231,000. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. Pay ranges outside Redwood City are adjusted based on cost of labor in each respective geographical market. Your recruiter can share more about the specific pay range for your location during the hiring process.

#LI-Hybrid