• 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

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 worth in money — COGS avoided, net of the revenue you destroyed and the support cost you created. Keep that number current.
• Run holdouts even on enforcement, wherever it is legal and safe to do so, because otherwise you will never know what your rules are worth.
• Monitor for adaptation , not just drift. Model decay here is caused by an adversary, and it looks different: a signal stops firing, a ring changes shape, a cohort's behaviour shifts inside a week.
• Watch your own inputs. A detection system that silently loses a feature degrades to a system that cannot reach its own threshold — and reports nothing wrong while doing it.

Making it usable

• Ship scores and reasons, never scores alone. An analyst, a support agent or an auditor must be able to read why an account was actioned.
• Produce evidence that survives outside the company. Our abuse work has already been used in an external audit and in Terms-of-Use enforcement; write for that standard.
• Work in SQL, Python and BigQuery on raw event, payment and generation data. We use AI heavily — resourcefulness beats syntax.

Who We're Looking For

• Experience building abuse, fraud or trust-and-safety detection that actually took action on real users — payments fraud, promo abuse, multi-accounting, bonus abuse, account takeover, marketplace or gaming abuse. Anti-money-laundering and credit-risk backgrounds transfer if the modelling was yours.
• Genuine understanding of the adversarial setting: that your counterparty responds, that your training labels are generated by your own past decisions, and that offline AUC on a censored label set is close to meaningless.
• Strong SQL and Python on raw event data without help: joins across payments, usage and identity, window functions, cohort replay. Gradient boosting, and the judgment to know when a two-condition rule beats a model.
• Comfort with graph and linkage thinking — connected components, shared-attribute clustering, and an instinct for when a link is evidence and when it is coincidence.
• Precision-first evaluation instincts: precision at the operating point, cost-weighted error, and why you would take 20% recall at 90% precision over the reverse in an enforcement system.
• Unit-economics literacy: gross margin per account, what compute actually costs us, why a negative-margin customer may be entirely legitimate.
• The judgment to escalate rather than act when the evidence is thin, and to say so in writing.
• Clear written and spoken English, B2+.

• Fraud / risk data scientists from fintech, payments, marketplaces, gaming or crypto
• Trust & safety and platform-integrity teams who owned detection and enforcement policy
• Growth or product data scientists who ended up owning promo-abuse because nobody else would
• Analysts from payment processors, PSPs or chargeback/dispute operations who moved into modelling

What This Role Is Not

• Want to build content-moderation or NSFW classification — that is a different function here
• Would ship a ban list without knowing its precision
• Optimize AUC on a label set your own enforcement created, and call it validated
• Need a labelled fraud dataset to exist before you can start
• Treat every negative-margin account as an abuser
• Are uncomfortable that being right 90% of the time means being wrong about real, paying, legitimate people — and that you own what happens to them
• Want predictable 9–5 workdays

Hiring Process

• Screening call (30 min)
• Hiring-manager screen (30 min)
• Business case (1 hour)
• Practical home task (7 days)
• Team interview (60 min)
• Paid on-site trial (1 month)

The Deal

• Competitive salary in USD
• Equity: participation in the company's stock option program
• On-site role in Almaty, Kazakhstan
• Relocation support (flight + temporary housing)
• Flat structure, high autonomy, fast career growth