Data Science, Product Analyst

Job description

**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 **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. This is a fully on-site role based in our **Almaty office**. Our team works from the office **five days per week for the full working day**. We believe in-person collaboration is an important part of how we move quickly, solve complex problems, and build strong teams.