Outset invented a better way to do research: AI-powered user interviews.
Global brands like Hubspot, Microsoft, Uber and Nestle use Outset to achieve deeper, qualitative insights about their users at unprecedented speed and scale.
The research industry is massive (>$140B and growing) and ripe for disruption. Incumbents are outdated, slow, and burdened by high-cost services. That's our opportunity.
Outset is backed by top Silicon Valley investors and raised a $30M Series B in December 2025—less than six months after our Series A—led by Radical Ventures with participation from M12 (Microsoft’s venture arm), 8VC, Y Combinator, and Adverb Ventures.
The raise follows a breakout year, with our business growing 8x as enterprise customers across industries adopt our category, AI‑moderated research, as the new standard for understanding people.
We’re a tight-knit team serving some of the world’s largest enterprises.
This analyst turns Outset's own data into decisions faster than a traditional analytics hire would, by using AI tools as a core part of the workflow rather than bolting them on afterward. They're as comfortable writing a SQL query to understand critical business data accurately, as they are building a small internal tool, script, or AI skill that answers a recurring question without a human re-running it every week.
The job is part analysis, part automation - someone who'd rather build the dashboard-that-builds-itself than ship the same report by hand twice. Improving processes and workflows across the company excites them and they love to build tools to help internal teams across the business move, manage and understand data.
Responsibilities
Answer recurring business questions across GTM, People, Product and Engineering using SQL, Python, and whatever AI tooling gets there fastest.
Build small internal tools - vibe coded apps, Slack bots, scripts, lightweight dashboards, AI skills - that eliminate a manual report or a repeated ad hoc request.
Partner with stakeholders to figure out what they actually need before building anything.
Maintain and improve data pipelines so the underlying numbers are trustworthy, not just the tools on top of them.
Evaluate new AI tools and workflows as they come out, and decide which are actually worth adopting internally vs. hype.