Posted May 12

San Francisco



Our mission is to upgrade the Internet access in every public school classroom in America so that every student has the opportunity to take advantage of the promise of digital learning

EducationSuperHighway is the leading non-profit focused on upgrading the Internet infrastructure in America’s K-12 public schools. We believe that digital learning represents an unprecedented opportunity to provide every student with a world-class education and that every school requires high-speed broadband to make that opportunity a reality. EducationSuperHighway’s data-driven programs help accelerate upgrades in America’s schools. We work to raise awareness of the school connectivity gap, provide technical and procurement expertise to states and districts, and advocate on behalf of students to influence policy decisions. Our work has helped shape President Obama’s ConnectED initiative and served as a catalyst for modernization of the Federal Communications Commission’s $2.4 billion E-rate program.

The Role:

Analysts are responsible for providing insightful, rigorously-validated, and repeatable solutions to the strategic problems that EducationSuperHighway is attempting to tackle at a national, state, and district level. You are a gifted communicator with the ability to help others understand gritty research questions and define a structured approach to providing data driven solutions that help them meet strategic goals. You believe that data and analyses are critical to good decision-making and that algorithmic approaches to problem solving are essential for rapid scaling and automated solutions. As a result, you have spent most of your career to date analyzing, interpreting, and strategically applying data and analyses.

You go above and beyond in your analysis to answer questions at the goal-level, not just the data-level. You are critical about data’s imperfections but creative about using it to drive to insight. For example, when data answers one question, it inevitably asks several more, and you continue that conversation until it has helped you reach a conclusion that matches your real-world judgment and has actionable implications. You have a solid understanding of statistics - it is a science that you apply to all of your work. You have also worked enough with data to know that it is often neither reliable nor robust, and you are very accustomed to creatively using what you have, combined with qualitative assessment, to reach conclusions. You take pride in being rigorous, practical, and creative.


  • Explore and prototype machine learning approaches to the prediction and classification of accounting records.
  • Research, develop, integrate, test, and document solutions to address challenging operational scenarios for the use of E-rate records
  • Develop analyses, including statistical and machine learning approaches, to support the implementation of our fiber and affordability programs
  • Must have experience designing analytical experiments with learning algorithms
  • Must have experience measuring effectiveness of results
  • Familiar with Machine Learning tools/capabilities


  • Bachelor’s degree, preferably with a quantitative background in either statistics or data science
  • 2-4 years experience as an analyst in a business strategy, finance, education, or public policy context; management consulting and/or finance background preferred
  • Aptitude for big-picture thinking combined with an impeccable attention to detail
  • Passion for making meaning of large, complex, imperfect data sets
  • Intellectual curiosity that drives a rigorous, action-oriented problem-solving approach
  • Ability to draw actionable insight from quantitative analysis
  • Basic knowledge of predictive analytics and machine learning methodologies and a willingness to further develop these skills
  • Expertise with at least one statistical computing package (R preferred) and PowerPoint proficiency required; experience with data visualization, scripting, and statistical analysis is a bonus

How to Apply

Apply here

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