Staff AI Product Analyst
💰 $165,000 – $225,000/yr
At a glance
- Who can apply
- Worldwide
- Work arrangement
- Remote · Worldwide
- Employment type
- Full-time
- Salary
- $165,000 – $225,000/yr · published by the employer
Checked against the employer's source on October 15, 2026. Details come from the employer's posting; confirm them on the application page.
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Job Description
About Angi
For over 30 years, Angi has powered the future of the home services industry. Founded in 1995 as Angie's List and rebranded in 2021, Angi is a global company with 9 brands in 8 countries and employees worldwide. Homeowners have turned to Angi for 300 million home projects and counting, connecting skilled professionals with reliable work opportunities.
About the Role
The Staff AI Product Analyst will define AI-native product analytics for Angi, turning visionary technology into prioritized use cases, robust evaluation disciplines, and a trusted data ecosystem. This role is central to building Angi's agentic product analytics capability—ensuring both human teams and AI agents remain aligned on truth, quality, and usefulness.
As a senior individual contributor, you will have broad influence across the product analytics function and beyond. The ideal candidate is a systems-thinking data expert who bridges deep AI capabilities with practical business outcomes.
Key Responsibilities
AI Product Strategy & Metrics Design
- Partner with AI product, engineering, and domain experts to define north-star and guardrail metrics for flagship AI journeys, focusing on adoption, task completion, quality, and downstream business outcomes
- Align closely with data science partners on test design, interpretation, and causal framing when new AI products ship
- Map how agents interact with metrics, raw tables, documentation, and APIs to ensure requirements for data and analytics engineering are explicit, structured, and testable
Evaluation, Governance & Backlog Management
- Design and maintain formal evaluation sets, golden datasets, rubrics, and regression checks for agent outputs to guarantee accuracy, completeness, policy safety, and usefulness
- Systematically categorize wrong answers, tool misuse, context gaps, and data edge cases, feeding insights back into prompts, retrieval systems (RAG), tools, and data modeling pipelines
- Monitor live usage of agentic AI analytics workflows—tracking success, abandonment, and fallback patterns
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