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 before explaining the movement - a surprising number is a claim, not a fact.

• Ownership: you decide what's worth analyzing, you chase the fix, you follow the recommendation to a shipped change.

• Clear written and spoken English, B2+.

Backgrounds that often do well:

• Product analysts from consumer subscription or PLG products

• Growth/BI analysts who moved into product and stayed

• Early-startup analysts who built product measurement from zero

• Data scientists who got tired of models and want decisions

What This Role Is Not

This role is not a fit if you:

• Wait for a ticket to tell you what to analyze

• Want to build models more than you want to change decisions

• Need a data engineering team and clean tables to exist before you can start

• Would report a lift you don't believe in because a stakeholder wants it

• Need perfect data before you can say anything useful

• Want predictable 9–5 workdays

Hiring Process

We move fast:

1. Screening call (30 min)

2. Technical interview & case study (1 hour)

3. Practical home task (7 days)

4. Team interview (60 min)

5. Paid on-site trial (1 month)

What We Offer

• Competitive base salary in USD, based on your experience, skills, and the scope of the role.

• Equity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth.

• Relocation support to Almaty for candidates moving from another city or country.

• A highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company.

• Opportunities for professional growth, ownership, and career development as the company scales.

• Company-provided equipment, meals, transportation, or other office benefits.