• We know our true authorization rate — at the unit the business actually cares about, not the one that is easy to compute
• Every basis point of payment cost has a name, a geography and an owner
• A decline is a diagnosis, not a statistic
• When we propose a new rail, a new entity or a new processor arrangement, the number underneath it holds up to Stripe's own analysis and to our board's

Authorization & decline analysis

• Own authorization rate end to end: by country, issuer, BIN, card brand and funding type, plan price point, first payment vs renewal, and payment method.
• Build and maintain the decline taxonomy — what each decline code actually means, which are recoverable, which are terminal, and which are us rather than them.
• Measure at the right unit . An invoice that succeeds on the third retry is a success, not two failures and a success, and reporting it the other way has made teams optimise the wrong thing.
• Separate soft declines we can recover from hard declines we cannot, and put a number on the recoverable pool.

Cost of acceptance

• Own the blended take rate and its decomposition: interchange, scheme fees, processor markup, cross-border and currency-conversion components, disputes, and the fixed per-transaction pieces.
• Find where the cost actually sits. Averages hide it — the cost is concentrated by corridor, by card type and by whether the transaction is domestic.
• Build the evidence for commercial conversations with our processor, and keep it current enough to reuse.

Local payment methods, entities and routing

• Size the case for new rails market by market: what share of local commerce runs on them, what our current card performance costs us, and what we would realistically recover.
• Model merchant-of-record and local-acquiring arrangements against the alternative of our own entities, honestly — including the fee we would pay for the privilege.
• Design and read the experiments that prove it, with the product analytics team. A new payment method is an A/B test, not a launch.

Renewals, dunning and involuntary churn

• Own the retry and dunning strategy as a measurable system: when to retry, how often, through which rail, and when to stop.
• Quantify involuntary churn separately from voluntary. They look identical in a cancellation count and they need completely different fixes.
• Track the health of stored credentials — network tokens, card-account-updater coverage, expiring cards — because in a subscription business that is silent revenue leakage.

Disputes, refunds and reconciliation

• Own dispute and chargeback rates against network thresholds, with the lag handled properly — disputes arrive weeks after the charge and a same-period denominator understates you.
• Reconcile processor data to the revenue ledger and explain the differences rather than plugging them.
• Work with the antifraud team where disputes are abuse rather than genuine dissatisfaction.

How we work

• SQL and Python on raw processor and billing data in BigQuery. We use AI heavily — resourcefulness beats syntax.
• Partner with Finance (the numbers get booked), Product (checkout and paywall), Engineering (the billing integration) and our processor's team directly.

Who We're Looking For

• 2+ years in payments analytics, payment operations, billing, or risk at a merchant, PSP, acquirer, or fintech. This is a domain role — general analytics experience without payments does not transfer quickly enough.
• Fluency in the actual mechanics: authorization vs capture vs settlement, decline codes, 3DS and SCA, network tokens and account updater, interchange and scheme fees, chargeback lifecycle, MCC, BIN, cross-border and DCC.
• Strong SQL, without help. Most of this job is joining sessions to intents to charges to invoices to refunds across imperfect keys and getting the denominator right.
• Python at working level for analysis.
• The instinct to check the unit of measurement before quoting a rate — per attempt, per invoice, per user, per session are four different numbers and only one of them answers the question.
• Comfort with subscription billing specifically: renewals, proration, plan changes, failed payments, dunning, involuntary churn.
• Commercial judgment: you can build the case for a rail or a rate and defend it against a vendor who does this for a living.
• Clear written and spoken English, B2+.

• Payment operations or payments analytics at a consumer subscription, marketplace or gaming company
• Analysts from PSPs, acquirers, orchestration platforms or card networks
• Billing / revenue operations analysts who owned dunning and involuntary churn
• Risk or chargeback analysts who moved toward the acceptance side

What This Role Is Not

• Want to own checkout UX or pricing — that is Product Analyst, and it is open
• Want to build fraud models — that is the Antifraud data scientist, and it is open
• Would report an authorization rate without saying what the denominator was
• Would take a processor's own analysis of their own performance at face value
• Need someone else to prepare the data
• Want predictable 9–5 workdays

Hiring Process

• First interview (30 min)
• Business case (60 min)
• Take-home with real-shaped data (7 days, ~10–12 hours of work)
• 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