Mixed Methods User Researcher

Even Up is on a mission to close the justice gap using technology and AI. We empower personal injury lawyers and victims to get the justice they deserve. Our products enable law firms to secure faster settlements, higher payouts, and better outcomes for victims injured through no fault of their own in vehicle collisions, accidents, natural disasters, and more.

We are one of the fastest-growing vertical SaaS companies in history, and we are just getting started. Even Up is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, Signal Fire, and Lightspeed. We are looking to expand our team with talented, driven, and collaborative individuals who seek to have a lasting impact. Learn more at

We're looking for a technically sharp, operationally scrappy Mixed Methods User Researcher to be the engine that connects user research and feedback to product execution. Sitting close to our Product, Engineering, and Design (EPD) teams, you'll turn a rich and ever-growing repository of user data — product usage, feedback, interviews, sentiment — into clear themes, pain points, and recommendations that shape what we build.

This is a hands-on, full-stack analyst role: you'll run your own quantitative and qualitative research, and build the lightweight tools, automations, and research-ops infrastructure that make insights self-serve for everyone else. You'll be just as comfortable writing SQL and standing up an AI-powered feedback pipeline as you are running a user interview or presenting a recommendation to leadership. It's an ideal role for someone analytical and user-obsessed.

• Mine & synthesize user signal: Use SQL, BI tools, and AI/low-no-code tooling to analyze user engagement and feedback across product areas, surfacing key themes, pain points, and feature requests for the EPD org.

• Run mixed-methods research: Design and execute lightweight research — surveys, 1:1 interviews, focus groups, sentiment analysis/benchmarking — pairing the quantitative "what" with the qualitative "why."

• Build self-serve tooling & research ops: Implement AI and no-code tools that make feedback discoverable and insights self-serve, and stand up the scrappy operational backbone (participant sourcing, tooling, repeatable workflows) research needs to scale.

• Connect insight to decisions: Translate findings into tight, well-reasoned recommendations that influence the roadmap early, separating good ideas from bad ones before significant engineering lift, and tie user pain points to adoption and revenue opportunities.

• AI-driven, self-serve feedback tools are adopted by the EPD team, cutting manual data-mining and speeding up identification of core product themes.

• Research outputs show an undeniable mix of quantitative rigor (usage, sentiment scores) and qualitative depth.

• Insights consistently inform the roadmap early and visibly shift team direction, rather than landing in a doc after decisions are made.

• Recommendations connect cleanly to feature adoption and ARR growth.

• Experience: 8-10+ years in a fast-paced SaaS / B2B / tech environment in an analytics, product analytics, or product research role.

• Data fluency: Strong SQL and BI/product-analytics tools (e.g., Tableau, Looker, Metabase, Mixpanel, Amplitude); comfortable pulling, manipulating, and interpreting data independently.

• AI tooling & automation: Proven ability to build and deploy low-code/no-code AI solutions to automate workflows and synthesize complex data.

• Mixed-methods research: Real balance of quantitative and qualitative methods, and the judgment to know which to reach for and why.

• Scrappy & resourceful: Willing to do the unglamorous work — direct Slack outreach to recruit users, building…