About ALSF


Childhood Cancer Data Lab (CCDL)
Powered by Alex’s Lemonade Stand Foundation (ALSF)

CCDL Overview

Located in the Philadelphia area, The Childhood Cancer Data Lab (CCDL) is a new program of Alex's Lemonade Stand Foundation (ALSF). The CCDL builds technologies that enable childhood cancer researchers to enlist big data to advance childhood cancer research. The CCDL is comprised of a team of software developers, data scientists, designers, and others who are driven to build software systems that address these needs. The CCDL aims to produce modular, open source, usable software. Members of the CCDL simultaneously contribute childhood cancer research and to the open source software community.

What is Alex’s Lemonade Stand Foundation?

Alex's Lemonade Stand Foundation (ALSF) emerged from the front yard lemonade stand of 4-year-old Alexandra “Alex” Scott, who was fighting cancer and wanted to raise money to find cures for all children with cancer. Her spirit and determination inspired others to support her cause, and when she passed away at the age of 8, she had raised $1 million. Since then, the Foundation bearing her name has evolved into a national fundraising movement. Today, ALSF is one of the leading funders of pediatric cancer research in the U.S. and Canada having raised more than $150 million so far, funding nearly 1,000 research projects and providing programs to families affected by childhood cancer. For more information, visit AlexsLemonade.org.

About the Position


  • Candidates are expected to have an MD, PhD, or equivalent, with either
    • A strong background in computer science, machine learning, statistics, genetics, bioinformatics, or closely related field and programming experience with attributable contributions to source code and a primary interest in applying these skills to pediatric cancers.
    • Deep expertise in pediatric cancer with some experience in biological data science and a willingness to grow their skill set in this domain.
  • The ideal candidate will have a track record of scientific productivity and leadership and will strive for robust and reproducible analytical workflows.


To apply, send a cover letter that includes the names and contact information for three references, a short statement of research interests, and a current CV to [email protected].