**Must have exposure to Amplitude, Mixpanel, PostHog, Heap or equivalent analytics tools**

You will be contacted only if your profile is found suitable to the job role.

Job Description:

This is a hands-on, high-ownership role for someone who wants to build a product-analytics practice, not inherit one. You'll define the metric system for an AI-native platform, design the instrumentation behind it with engineering, and turn raw usage into the questions and answers that shape the roadmap. Because the core action on our platform — an agent execution — is non-deterministic, this goes beyond classic SaaS analytics. You won't just measure whether a feature was used; you'll measure whether it worked: task success, quality, human intervention, and the cost of getting there. You'll be the person who can walk into a prioritization discussion and change what gets built, improved, scaled, or killed — with data.

What You'll Do:

Define the Measurement System

● Build the core metric tree from scratch — North Star plus the input metrics that move it - for an agentic, self-serve platform.

● Define activation, adoption, retention, engagement, and expansion metrics that hold up to scrutiny and that PMs actually trust.

● Establish account/team-level adoption rollups for a B2B, product-led motion.

Own Instrumentation Quality

● Partner with engineering to design event taxonomies and tracking plans for the full agent lifecycle — creation, configuration, testing, deployment, and execution.

● Audit and close instrumentation gaps so downstream analysis is trustworthy by default. Analyze Funnels and Friction

● Map the agent-creation and agent-execution funnels and pinpoint where users stall, abandon, or fail.

● Turn friction points into prioritized, quantified opportunities for Product and Design.

Measure Value and Outcomes

● Connect product usage to business outcomes — value delivered, cost incurred, and unit economics per agent/execution.

● Identify which agents, features, and workflows create measurable value, and which quietly don't.

Measure Agent Quality and Feedback Loops

● Define proxies for task success, output quality, and human-in-the-loop review/override rates.

● Build the feedback-loop measurement that tells Product and Engineering where agent quality is strong, weak, or degrading — and feed it back into the roadmap.

Retention, Expansion, and Risk Signals

● Define leading indicators of retention, product-led expansion, and churn risk.

● Surface these signals to Product, Customer Success, and Sales/Solutions so they can act early.

Influence the Roadmap

● Translate ambiguous product questions into crisp, measurable analysis.

● Deliver recommendations — with clear evidence — on what to build, improve, scale, or deprioritize, and make analytics a standing input to prioritization.

Partner Across Functions

● Work closely with Product, Engineering, Design, Customer Success, Sales/Solutions, and Implementation to make sure the right questions get asked and the answers get used.

Must-Have Skills:

● 3–7 years in product analytics for a B2B SaaS or platform product — ideally with a self-serve / product-led growth motion.

● Confident, independent SQL — you write complex queries (cohorting, funnels, window functions) without hand-holding.

● Hands-on ownership of a product analytics platform — Amplitude, Mixpanel, PostHog, Heap, or equivalent.

● Demonstrated track record defining product metrics from scratch — activation, retention, engagement, funnels — not just consuming existing dashboards.

● Experience designing instrumentation and event schemas in partnership with engineering.

● Ability to translate ambiguous product questions into structured analysis and clear recommendations.

● Evidence of influencing roadmap or product decisions with data.

● Strong product judgment and the communication skills to make analysis land with senior stakeholders.

Good-to-Have / Bonus Skills:

● Experience with AI-native, agentic, LLM, copilot, or workflow-automation products — a strong differentiator, not a requirement.

● Experience with builder / creation products or developer / no-code / low-code platforms, where measuring how people build things matters.

● Experience measuring AI output quality, task success, evaluation, or human-in-the-loop review.

● Unit-economics / cost-per-usage analysis for consumption-priced products.

● Data modeling (dbt), warehouse experience (Snowflake / BigQuery / Redshift), BI tools (Looker / Metabase / Tableau).

● Python / pandas for deeper analysis.

Senior Product Analyst, Experience Foundation

Senior Product Analyst