Staff AI Product Analyst

For over 30 years, Angi has powered the future of the home services industry, creating an environment where homeowners and pros benefit from more jobs done well.

For homeowners, our platform is a reliable way to find skilled pros. For pros, we're a reliable business partner who helps them find the winnable work they want, when they want. For employees, we're an amazing place to call home. We can't wait to welcome you.

Angi at a glance:

Founded in 1995 as Angie’s List and rebranded in 2021

Global company with 9 brands in 8 countries and employees worldwide

Homeowners have turned to us for 300 million home projects and counting

About the team

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—defining where AI-native analytics creates real leverage, designing the evaluation frameworks that earn organizational trust, and establishing repeatable system standards. As a senior individual contributor, you will have broad influence across the product analytics function and beyond, ensuring that both human teams and AI agents remain aligned on truth, quality, and usefulness.

The ideal candidate is a systems-thinking data expert who bridges the gap between deep AI capabilities and practical business outcomes.

What you’ll do

AI Product Strategy & Metrics Design

Flagship Journey Measurement: 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.

Causal Framing & Experimentation: Align closely with data science partners on test design, interpretation, and causal framing when new AI products ship.

Data Layer Systems Thinking: 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

Eval Framework Architecture: Design and maintain formal evaluation sets, "golden datasets," rubrics, and regression checks for agent outputs to guarantee accuracy, completeness, policy safety, and usefulness.

Taxonomy & Failure Mode Analysis: Systematically categorize wrong answers, tool misuse, context gaps, and data edge cases, feeding those insights back into prompts, retrieval (RAG), tools, and data modeling pipelines.

Performance Monitoring: Monitor live usage of Agentic AI analytics workflows—tracking success, abandonment, fallback patterns, and human override rates to maintain a prioritized engineering improvement backlog.

Cross-Functional Leadership & Enablement

Playbooks & Narrative: Own internal documentation defining when to use agents, required human checkpoints, and how to communicate uncertainty. Support enablement and leadership storytelling around agentic AI analytics.

Strategic Collaboration: Partner with business leaders, the Principal Analytics Engineer, and data engineering teams to translate complex AI capabilities into measurable business outcomes.

Defining Success: Deliver recurring measurement artifacts (dashboards, reviews, or written readouts) that leadership uses to prioritize roadmaps, while continuously reducing high-severity incidents where agents produce incorrect metrics.

Who you are

Minimum Qualifications