Mixed Methods User Researcher, EvenUp

• Experience: 5+ 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 research ops from scratch — without waiting for a pre-existing machine
• User-centric & business-minded: You can draw a straight line from user behavior to product decisions and revenue, and you're passionate about getting the voice of the customer into execution
• Strong communicator: You produce clear, consumable artifacts (not just dense reports) that make stakeholders actually use your insights

Desirable

• 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
• ---How Success Is Measured---
• 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