Data Scientist

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
Right now we treat 25M+ people as one user. The same paywall, the same discount, the same lifecycle email, the same retention effort. And we mostly conclude that a campaign worked by looking at what the people we sent it to did next — which is not the same thing. This role makes the company act per user, and know what that action was worth. You own both halves of that: the models that decide who we aim at, and the causal reads that say whether it landed. Who is about to leave. Who will come back on their own without us spending anything. Whose decision a discount actually changes. And what a payer is worth over their life, once the discount, the refunds and the compute they consume are all inside the number. You make sure:
Discounts and credit vouchers go to the people whose behaviour they change, and nowhere else

We know who is about to churn early enough to do something about it — and we know whether the something worked

Lifetime value is a number we can defend by cohort, by plan and by the discount a cohort was acquired on, not one blended average that hides all three

Every model and every campaign has a holdout behind it, so we can always state what it is worth in money

What You Will Do
Churn, retention & lifecycle propensity
Build churn and downgrade prediction for subscribers, and repeat-purchase propensity for one-time credit-pack buyers — two different machines, not one model with a flag on it.

Know the difference between a model that predicts churn and a model that reduces it. AUC earned by detecting users who already stopped using the product is worth nothing.

Hunt leakage relentlessly. Cancellation-adjacent features will hand you a beautiful offline number and a useless system.

Pair every score with an intervention and a holdout. The deliverable is a retained user, not a ranked list.

Uplift modelling & offer targeting
Own incrementality on everything we aim at a user: discounts, credit vouchers, trials, upgrade prompts, win-back campaigns, retention saves.

Model uplift, not propensity. Targeting the users most likely to convert spends margin on people who were going to convert anyway; the entire value of this work is finding the users whose decision the offer changes.

Design the randomized holdouts that make uplift trainable and measurable in the first place, and keep them running permanently — including the ones marketing will ask you to switch off.

Respect the guardrails. An offer model optimizing conversion alone will happily find the accounts that convert at negative gross margin, and it will find the abuse rings first. Margin and abuse signals are constraints on the objective, not a later cleanup.

Work with Legal on what may be personalized. Personalized pricing and discounting touches consumer-protection and consent rules in several of our markets — you should want that conversation, not route around it.

Lifetime value & payer economics
Own LTV: cohort-based, censored honestly, stated on contribution margin rather than gross revenue, and split by plan, segment and acquisition discount.

Model the parts that actually move it — refunds and chargebacks, credit breakage and expiry, plan migration and downgrades, and the compute an unlimited plan consumes.

Make it a decision input, not a slide: what we can pay for a user, which discount depth pays back, which plan we should stop selling.

Keep it current and versioned. A stale LTV cur