Data Scientist, Product
Higgsfield · Almaty
Why work at Higgsfield AI?
Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B 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 curve is worse than no curve, because people act on it.
Experimentation & causal measurement
Design and read the experiments behind pricing, packaging, offers and lifecycle — with the product analytics team, not in parallel to it.
Reach for the right tool when a clean experiment is impossible: difference-in-differences, matching, switchback and geo tests, synthetic control — and name the assumption each one is buying.
Separate "users who do X retain better" from "making users do X improves retention." Every time, out loud.
Kill your own results. A finding that doesn't survive a second look should die at your hands, not in a board deck.
How we ship
Own the work end to end: framing the decision, features, training, evaluation, the scored audience landing in the tool that sends the offer, monitoring, retraining.
Models ship as batch scores and policy rules into lifecycle tooling, the paywall and pricing — plus the readout that says what they earned. There is no real-time serving stack to babysit.
Monitor for drift. New model launches, price changes, discount campaigns and seasonality all move the ground under a deployed score.
Write down what each model is worth, in money, and keep that number current.
Who We're Looking For
Experience shipping models that changed what the business did to real users— a targeted campaign, a pricing rule, a retention programme. Notebooks and offline benchmarks are not this.
Depth in at least two of: churn and propensity modelling, uplift & causal ML, LTV and subscription economics, experimentation at scale.
Genuine causal literacy: randomized holdouts, incrementality, Qini and uplift curves, selection effects, and why a lift measured before-and-after is usually not a lift.
Strong SQL and Python: cohorts, funnels and retention on raw event data without help; gradient boosting; and the boring parts — feature pipelines, retraining cadence, a scoring job that runs on a schedule and doesn't rot.
Understanding of subscription plus one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, the deferred value of unspent credits, and why revenue and margin disagree about the same customer.
Commercial judgment: you name the decision and the metric before you pick the model, and you know when the answer is a rule rather than a model.
Pragmatism. A segment rule shipped this month that lifts retention beats a two-quarter uplift platform. Then you replace the rule.
Clear written and spoken English, B2+.
Backgrounds that often do well:
Growth, retention or monetization data scientists from subscription, gaming, fintech or e-commerce — anywhere offers and churn are modelled with real money attached
CRM and lifecycle data scientists who owned campaign targetingandits incrementality
Causal inference and uplift specialists who ship policies rather than papers
Strong product analysts who moved into modelling and kept the decision instinct
What This Role Is Not
This role is not a fit if you:
Want to train or fine-tune generative video models — that's what our R&D ML Engineer roles do, and they're open
Want to own a recommender or a serving stack — ranking is not in this role's scope today
Would target an offer at the users most likely to buy and call it personalization
Report a lift from a before-and-after comparison, or from comparing the users you targeted to everyone you didn't
Optimize AUC and hand the score to someone else to work out what to do with it
Need clean labelled data, a feature store and an experimentation platform to exist before you can start
Want predictable 9–5 workdays
Hiring process
We move fast:
Screening call (30 min)
Technical interview & case study (1 hour)
Practical home task (7 days)
Team interview (60 min)
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.