Project Objective

Collect and synthesize data on the knowledge, skills, and attributes most predictive of an ideal Roblox employee for use in the Robots hiring assessment.

Methodology

Generative - Qualitative

User Interviews

Study Overview

I conducted a series of semi-structured interviews across the Roblox organization - including members, managers, and directors of teams spanning data science, art, and talent acquisition - to elucidate what knowledge, skills, and attributes (KSAs) are perceived as the most beneficial for Roblox team member job performance.

Care was taken in developing the interview script and conducting the interviews to get at the answer to these questions 1) from varying perspectives, and 2) in ways that would allow the interviewee fully expand on their thoughts in responses to open-ended questions without priming., and 2) in ways that would allow the interviewee fully expand on their thoughts in responses to open-ended questions without priming.

After being prompted to describe their roles, responsibilities, team dynamics, and day-to-day activities in great detail, interviewees were prompted to begin considering KSAs. I not only asked what KSAs they thought were important in daily job performance, but encouraged them to consider what they'd look for in a potential hire at multiple levels, how they would assess these KSAs in an applicant, and what KSAs are important in their own jobs. Once interviewees had elaborated at length on their answers to such questions, I probed interviewees on more specific questions about my team's KSAs of interest.

While I was not involved in the synthesis and application of the information derived from these interviews, my interviews were eventually critical to the successful development of the game-based Robots assessment.