Data Science, Product Analyst

Why work at Higgsfield AI?
Higgsfield AI is the fastest-scaling generative AI company in history, hitting $500M in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.
What This Role Means at Higgsfield
This role owns how Higgsfield measures its product - from the first sign-up to whether people come back.
You are the measurement owner for onboarding, first generation, the paywall, plans and credits, and retention. Product managers ship fast here - your job is to make sure they ship knowing.
You make sure:
Every product decision has a number attached to it before it ships and after it ships

Experiments are designed to be readable, and then read honestly - including when the answer is “this didn’t work”

The events the product emits can be trusted; you own the definition, not just the query

A PM gets an answer in hours, not next sprint

You are the person who decides what “it worked” means.
What You Will Do
Product measurement
Own the core product metrics: activation, first-generation success, generation depth, free→paid conversion, repeat usage, retention by cohort and segment.

Define each metric once, write the definition down, and hold the line on it - one definition across dashboards, decks, and Slack.

Instrument new features before launch, with product and engineering: which events, which properties, what success looks like, when we call it.

Watch the health of the event stream itself - tracking drift, double counting, missing parameters, naming breakage and find the problem before a decision gets made on top of it.

Experimentation
Design A/B tests properly: hypothesis, one primary metric, unit of randomization, MDE, sample size, run length, guardrail metrics.

Read them honestly: significance, peeking, novelty effects, sample-ratio mismatch, segment heterogeneity.

Separate “we proved this works,” “we proved this doesn’t,” and “we can’t tell from this test” - and say which one out loud.

Product & monetization analysis
In-product funnel work: onboarding and quiz, first generation, paywall, checkout, plan choice, credit top-ups.

Pricing and packaging analysis: plan mix, credit consumption, unit economics per generation, margin by feature and by model.

Feature adoption and its real effect on retention and revenue — separating “users who do X retain better” from “making people do X improves retention.”

Behavioural segmentation: casual creators vs corporate/B2B, new vs returning, by model and by use case. Mixing them hides everything that matters.

Making it usable
Build the few dashboards PMs actually open on their own — decision-shaped, not comprehensive.

Write short readouts a non-analyst can act on. Visualize data for humans, not for other analysts.

Automate recurring reporting so your hours go to new questions, not refreshes.

Work in SQL, Python, BigQuery and product analytics tools. We use AI heavily - resourcefulness beats syntax.

Who We're Looking For
We're looking for an analyst with opinions.
You should have:
Strong SQL: window functions, cohorts, funnels and retention on raw event data, without help.

Python at working level for analysis (pandas, notebooks). Entry level is fine - we use AI heavily.

Real experimentation experience: you have designed tests, run them, and killed features with the results.

Statistical honesty: you know what a p-value does and does not entitle you to say, and you have refused to call a flat test a win.

Understanding of subscription + one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, deferred value of unspent credits.

The instinct to check the instrument befor