Data Scientist, Antifraud

Why work at Higgsfield AI?
Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ 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
Generative video has a problem most subscription businesses don't: every unit we sell costs us real compute. A stolen password costs a streaming service nothing. An account farm on an unlimited generation plan costs us GPU-seconds, at scale, every hour it runs.
We have found organised operators running hundreds of accounts on a single card, reselling unlimited generations, and harvesting per-account promotional grants at industrial scale. We have also found that most of our negative-margin accounts are not abuse at all — they are ordinary heavy users on an unlimited plan, which is a pricing problem wearing a fraud costume. Telling those two populations apart, at account level, before the money is gone, is this job.
You own the question of who is taking value they did not pay for, and the system that stops them.
You make sure:
We catch abuse before the compute is burned, not in a post-mortem that reconciles the loss

Every enforcement action has a measured precision, a measured cost of being wrong, and a way back for the customer we got wrong

The difference between fraud and unprofitable pricing is a number we can defend, not a vibe

When operators adapt — and they will, within days — we find out from a monitor, not from the monthly margin review

What You Will Do
Detection & scoring
Own the account-abuse scoring system end to end: signals, weights, thresholds, the scoring job, the monitoring, and the recalibration when it decays.

Build detection that fires early . A rule that catches 88% of the money on day 11 is worth far less than one that catches 60% at checkout, and you should be able to say why in dollars.

Work the two halves that actually matter together: linkage (who is connected to whom) and economics (what are they consuming). Either one alone produces a system nobody can switch on — linkage without economics flags families sharing a card, economics without linkage flags our best customers.

Treat cost as a gate, not a label. On an unlimited plan, being gross-margin negative is normal.

Entity resolution & ring detection
Find rings, not just accounts: shared payment instruments, IP and ASN concentration, email-stem families, synchronised registration bursts, behavioural fingerprints, automation signatures.

Do it without a reliable device fingerprint — we do not have one today, and part of this job is telling us what it would be worth and what it would cost.

Know where graph methods break. Transitive closure through a shared card will happily merge thousands of unrelated people into one "ring"; we have done exactly that and thrown the result away.

Enforcement, policy & the cost of being wrong
Design graduated, reversible actions — throttle, rate-limit, step-up verification, hold, pre-grant refusal, manual review queue, ban — and match the severity to the confidence.

Own the false-positive budget explicitly: how many paying customers are we willing to inconvenience to save a dollar of compute, and what is the appeal path for the ones we get wrong.

Build the pre-grant gates. The cheapest abuse to stop is the kind we decline to enable in the first place — a trial we do not grant costs nothing to claw back.

Partner with Payments on Stripe Radar, chargebacks and dispute economics; with Support on the queue your model creates; with Legal on what our Terms actually entitle us to do.

Measurement & adversarial monitoring
Quantify what the system is wo