Part-time Full Stack Developer, Social Science Prediction Platform

The Social Science Prediction Platform team at UC Berkeley, led by principal investigators Stefano DellaVigna (UC Berkeley) and Eva Vivalt (University of Toronto) is looking to hire a part-time full-stack engineer, as soon as possible, for the remainder of Fall 2021 and Spring 2022.

Project Description

We are looking to hire a computer scientist with web development experience to make updates to the Social Science Prediction Platform (SSPP). The SSPP facilitates the collection and cataloging of forecasts of research results. It uses the Qualtrics API to integrate surveys programmed in Qualtrics on the SSPP interface and a relational database to connect survey responses to project objects and user data such as contact information, demographics, and preferences.

Project Deliverables and Timeline

The SSPP has been built but requires updates including:

  • Improving the usability and navigability of the database structure for administrative users
  • Improving navigability of the project catalogue
  • Improving the visual presentation of survey results, as well as automated notification to respondents
  • Ensuring the Qualtrics and Sendgrid APIs continue to function well with evolving  platform features

We need a developer to work on these, as well as other minor improvements, for 5-10 hours/week possibly with a heavier load in the first 1-2 months, starting ASAP and working through Spring 2022. They should be able to join 2-4 1-hour virtual team calls per month. There may be opportunities for continued work following Spring.

Required Qualifications

  • Experience with database management
  • Strong SQL and Python/Django skills
  • Back-end development experience
  • Experience with DevOps (we use Heroku) and Docker

Preferred Qualifications

  • Experience building the back-end of a database or website in which datasets are being queried, manipulated, and transferred to users
  • Front end development experience (e.g. managing user credentials, building out intuitive user-interfaces)
  • Some understanding of empirical economic/data science methods
  • Commitment to producing clean, well-documented code (e.g., as evidenced by public GitHub repos)
  • Good communication skills, including ability to assess and communicate technical challenges and design decisions

Contact and How to Apply

Please send questions or statements of interest to Katherine Hoeberling ( and Aleksandar Bogdanoski (