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Outset is an AI-powered user research platform used by global brands like HubSpot, Microsoft, Uber, and Nestle to conduct qualitative research at scale. The company recently raised a $30M Series B (December 2025) and is experiencing rapid growth (8x business expansion). This Data Analyst role is unique in its emphasis on AI-augmented analytics: you'll use AI tools (Claude, ChatGPT, Copilot, Cursor) as a core part of your workflow to turn Outset's internal data into actionable decisions faster than traditional analytics hires.
You'll split your time between analysis and automation. Rather than shipping the same report manually each week, you'll build the systems that eliminate repetitive work. This means writing SQL queries to understand critical business metrics, building small internal tools (Slack bots, lightweight dashboards, scripts, AI skills), and automating recurring questions across GTM, People, Product, and Engineering teams.
Key responsibilities include:
- Answering recurring business questions using SQL, Python, and AI tooling
- Building internal tools and automations that eliminate manual reports and ad hoc requests
- Partnering with stakeholders to understand their actual needs before building
- Maintaining and improving data pipelines to ensure data quality and trustworthiness
- Evaluating new AI tools and workflows to determine which are worth adopting internally
- Documenting your work so others can run it independently
- Flagging data quality issues and inconsistent definitions early
You'll work in a tight-knit team at a Series B startup where the data stack is still being shaped, not inherited as mature infrastructure.
Qualifications:
- 3+ years in a data analyst, analytics engineer, or BI role with strong SQL and Python fluency
- Hands-on experience using AI tools (Claude, ChatGPT, Copilot, Cursor, or similar) to build something real—scripts, agents, automations—not just text summarization
- Builder's instinct: when a manual process repeats, your instinct is to automate it, not repeat it a third time
- Ability to move from vague stakeholder requests to specific, well-scoped answers with minimal hand-holding
- Clear written communication skills; can explain findings and tools to non-technical audiences concisely
- Experience at a startup where the data stack and tooling were still being built
- Exposure to building or prompting AI agents and workflows (beyond chat interfaces)
- Familiarity with modern BI/data tools (dbt, Looker, Hex, or similar)
