I'm Jared Bauer, a user researcher delivering insights that help teams build products customers love.
I've spent the better part of my career sitting at the intersection of how people work and how technology tries to help them — and increasingly, how AI is changing both of those things in ways that aren't always obvious from the outside. My work has taken me from academic research at the University of Washington to some of the most consequential developer tools in the world: GitHub, Atlassian, and Microsoft.
At GitHub, where I spent nearly six years as a Staff User Researcher, I led research on some of the company's most high-stakes bets — GitHub Copilot, GitHub Projects, and Copilot Code Review. My approach was always the same: start with the human, follow the behavior, and let the data tell you where the real problem is. That methodology led me to some surprising places. When industry voices were claiming that AI was degrading code quality, my randomized controlled trial with 243 developers told a different story — Copilot was actually improving it. But the deeper finding was more interesting: AI was quietly creating a new bottleneck downstream in code review, one that nobody had named yet. That insight helped reframe GitHub's product strategy and contributed to the public launch of Copilot Code Review, which now accounts for more than one in five code reviews on the platform.
I'm drawn to questions that don't have easy answers. What does it actually mean to trust an AI system? How do you measure developer productivity without oversimplifying it? When does AI augment a workflow, and when does it introduce friction somewhere else in the system? These aren't just research questions — they're the questions product teams need answered before they can make good decisions.
My methods span the full spectrum. I've run large-scale randomized controlled trials and intimate longitudinal diary studies. I've done thematic analysis of interview transcripts and K-means clustering of behavioral survey data. I've built research operations infrastructure from the ground up — participant panels, AI-enhanced insight repositories, automated incentive systems — because good research only compounds in value when the organization can access it. And I've translated all of it into product strategy, published research, and public narrative that reached developers, enterprise leaders, and the broader industry.
Before GitHub, I was at Microsoft on the Azure DevOps team, where I led pricing and value proposition research for Azure Artifacts and ran usability studies that contributed to a measurable lift in customer satisfaction. Earlier still, I was at Intel Labs, where research I contributed to resulted in two patents on context-aware and agent-based systems, and a CHI paper on mobile health that has since accumulated over 185 citations.
I also spent several years as an Affiliate Instructor at the University of Washington's Department of Human Centered Design and Engineering, mentoring student researchers and staying connected to the academic community that shaped how I think.
I hold a PhD in Information Science from the University of Washington, a Master's in Human-Computer Interaction from the University of Michigan, and a Bachelor's in Cognitive Science from UC San Diego. My academic work focused on distributed cognition and context-aware systems — which, it turns out, is a pretty good foundation for understanding what happens when you put AI in the middle of a complex human workflow.
I live in Portland, Oregon, with my family. When I'm not thinking about developer workflows, I'm probably out on my gravel bike.