Product Analytics Chapter Lead

About the Role

Product decisions at Finom are made fast, and we want them made on evidence. You join the Growth & Activation product stream as its analyst, working with PMs and engineers on how new customers discover the product, start using it, and grow with it.

There is no established way of doing product analytics here yet, so you define it: how experiments are designed, how metrics are named and counted, what a readout looks like. You come with your own approach rather than waiting for one to be handed to you.

What you'll do

• Work with the product managers and other stakeholders in the stream. Agree priorities with them, and look for the problems worth solving instead of waiting for a request to arrive.

• Bring recommendations to the business, including opportunities it does not see on its own.

• Define the metrics for the stream: what activation means, what we expect from the funnel, how each number is counted, and how it stays consistent with the way the rest of the company counts the same things.

• Design the tests. Hypothesis, primary metric, audience split, duration, and how the result gets read once it lands.

• Automate that work, so running a test is routine and not a small project every time.

• Build the infrastructure for testing at scale, with external tools or our own, so a testing culture can spread across the product teams.

What you bring

• Product analytics done end to end, with decisions that changed the roadmap behind it.

• Fluent in SQL, confident in Python, and solid on experimentation and causal methods/

• You have chosen or built tooling for tests and metrics before.

• You use AI tools in your daily work, not occasionally.

• A partner to stakeholders rather than a person who executes tickets.

• Proactive, you bring the problems and the hypotheses yourself.

• You take an ambiguous problem, structure it, and explain your reasoning to product and business people.

• English at C1 or above

Nice to have

• Product analytics built from scratch

• Experience where conversion volumes are low: B2B, long sales cycles, small samples.