At WayUp, the leading platform for connecting college students and young professionals to internships, part-time jobs, and entry-level roles, the technology recommends job listings to users immediately after they join the site and create a profile.
In this talk, I discuss how constraints from the business model and site design pushed us to build a custom system that matches the user's profile to job descriptions, even without the interaction data that drives most common recommendation systems.
With limited resources, but high expectations, it was important to make good architecture choices that would let the system scale up and evolve with business priorities. Some choices that have paid off include a two-step narrow-down approach using both business rules and machine learning, microservices and proper separation of concerns, careful choices of off-the-shelf technologies and tools, and designing for testability and iteration.
Harlan Harris has a PhD in Computer Science/Machine Learning from the University of Illinois at Urbana-Champaign, and worked as a Cognitive Psychology researcher before turning to industry. He has worked at Kaplan Test Prep, the Advisory Board Company, and startups including WayUp and Teachers Pay Teachers. Harlan also co-founded the Data Science DC Meetup and Data Community DC, Inc., and co-wrote Analyzing the Analyzers, a short O'Reilly book about the variety of data scientists. Follow him on Twitter and Medium at @harlanh.
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