Аналитика вакансии

— Идентификация и разработка новых бизнес-возможностей в области блокчейн. — Анализ рынка, конкурентов и потребностей клиентов для формирования стратегии развития продукта.

• Глубокая аналитика продаж - работа с большими массивами данных. Ты будешь выгружать, обрабатывать и анализировать информацию о продажах.

Senior Marketing Analytics Consultant в Improvado. 💎SQL, Looker, Power BI, or Tableau, English C1.

Основная задача — участие в миграции на Единый мидл: анализ существующих кредитных процессов и модулей, проектирование целевых сценариев взаимодействия, маршрутизации и обработки данных.

Location: Almaty, Kazakhstan Workplace: OnSite 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 Most subscription businesses lose a password. We lose GPU-seconds. When someone runs two hundred accounts off one card to harvest a per-account promotional grant, the cost lands on our compute bill the same hour, and it does not stop until somebody finds them. We have found operators doing exactly that — hundreds of accounts, synchronised signups, machine- generated emails, round-the-clock automation — and we have also found that most of our worst- margin accounts are not abusers at all, just heavy users on an unlimited plan. Telling those two apart, account by account, with evidence that survives being challenged, is this job. You are the person who finds them, proves it, and puts a number on it. You make sure: - Every enforcement action we take is backed by evidence a stranger could follow - We know what abuse cost us last month, on the same basis as last month - The detection system is measured against real cases, not against its own past decisions - When a ring changes shape, somebody notices this week — and that somebody is you WHAT YOU WILL DO Investigation & casework - Run ring investigations end to end: from a flagged account or a cost anomaly to a named cluster, its members, its signature, its lifetime economics and a recommended action. - Build the linkage yourself — shared payment instruments, IP and ASN concentration, email-stem families, registration bursts, behavioural and automation fingerprints — and know which links are evidence and which are coincidence. - Separate abuse from unprofitable-but-legitimate every single time. On an unlimited plan, negative gross margin is normal. Cost is a gate, never a trigger. - Triage the review queue the detection system produces, and feed what you learn back into it. Your adjudications are the only unbiased labels the scientist has. Evidence & defensibility - Produce evidence packages that hold up outside the company — to an external auditor, to a payment network, to a customer disputing a ban, to Legal enforcing our Terms. Our abuse work has already been used in all four contexts. - Write per-account, not per-ring, when the action is per-account. A ban list without a reason column is not evidence. - Know what our Terms of Use actually entitle us to do, and flag when the evidence supports the finding but not the action. Quantification & reporting - Own the abuse loss number: what it cost, on what basis, net of the revenue we reversed and the legitimate customers we caught by mistake. Same definition every month, written down. - Distinguish COGS burned, revenue at risk, revenue reversed and cash refunded — they are four different numbers and people will conflate them. - Build the recurring reporting that tells us whether the problem is growing, and the ad-hoc answer to "why did compute spike last Tuesday" that is due the same day. Monitoring & adaptation - Watch the detectors themselves. A rule that stops firing is a detection event, not a quiet week. - Check the instrument before explaining the movement: a missing upstream table, a silently dropped join, a vendor signal that went away. This has bitten us and it will again. - Spot new abuse patterns before they are in anybody's model — usually in a cost anomaly, a strange cohort, or a support ticket that does not fit. How we work - SQL and Python on raw payment, usage and identity data in BigQuery, without anyone preparing it for you. We use AI heavily — resourcefulness beats syntax. - Close partnership with the Antifraud data scientist (you validate what they build), Payments (chargebacks and processor signals), Support (the humans who meet your false positives) and Finance (the loss number goes in the accounts). WHO WE'RE LOOKING FOR - 2+ years doing investigative or risk analytics — fraud, abuse, trust and safety, AML, chargebacks, gaming or marketplace integrity. What matters is that you have chased real bad actors, not modelled them in the abstract. - Strong SQL, without help. Self-joins, window functions, cohort replay, and joining payments to usage to identity across messy keys. This is the single most-used skill in the role. - Python at working level for analysis — pandas, notebooks, a bit of graph work. - Investigative instinct: you follow the thread, you notice the thing that does not fit, and you can say when a pattern is a coincidence. - Evidentiary discipline: you can write up a finding so that somebody who distrusts you can check it, and you know the difference between "this looks like abuse" and "this is provable." - Unit-economics literacy: gross margin per account, what compute costs, and 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+. Backgrounds that often do well: - Fraud / risk analysts from fintech, payments, marketplaces, gaming, crypto or betting - Trust & safety investigators and platform-integrity analysts - Chargeback, dispute or AML analysts who write their own queries - Strong product or BI analysts who got handed the abuse problem and kept it WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to build models full time — that is the Antifraud data scientist, and it is open - Want to do content moderation or NSFW classification - Would put an account on a ban list without being able to say why in one sentence - Treat every negative-margin account as an abuser - Need a labelled dataset or a clean table before you can start - Are uncomfortable that your work gets real people's accounts restricted, sometimes wrongly - Want predictable 9–5 workdays HIRING PROCESS - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1 hour) - 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

Location: Almaty, Kazakhstan Workplace: OnSite 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 We sell subscriptions and credit packs to consumers in over a hundred countries, and today almost all of it moves as cross-border card payments in USD. That has three consequences, and this role owns all three. We are declined more than we should be. In some of our largest growth markets roughly one attempt in three fails, and a meaningful share of those are not "no money" — they are cross-border blocks, card limits, stale credentials on renewal, and issuers who do not recognise us. We pay more than we should. Our blended cost of acceptance is the single largest line below compute, and the majority of the network component sits on transactions our processor classes as cross-border. We are not on the rails our customers use. In two of our fastest-growing markets, the dominant local payment method is one we do not offer at all. You own the measurement and the argument behind fixing all of it. You make sure: - 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 WHAT YOU WILL DO 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+. Backgrounds that often do well: - 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 This role is not a fit if you: - 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 - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1 hour) - 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

- Опыт системного анализа от 5 лет, из них 2-3 года на лидирующих позициях; - Опыт менторства Junior и Middle аналитиков;

🧩 Solve a business case and apply analytical thinking to a real-world problem. 📊 Explore how data and analytics support business decisions at Mastercard.

• Приёмка моделей. Вы будете полностью вести приёмку новых версий моделей: определять, какие замеры нужны для конкретного релиза, запускать оценки, объединять результаты и готовить заключение о качестве модели.

В банковский проект по развитию кредитных процессов ищем бизнес аналитика. Основная задача — участие в миграции на Единый мидл: анализ существующих кредитных процессов и модулей, проектирование целевых сценариев взаимодействия, маршрутизации и обработки данных.

-Отвечать за поиск и анализ причин оттока / цементирования клиента в базе. -Оценивать эффект проектов, связанных со снижением оттока: потенциальные эффекты и постанализ после запуска.

– техническое образование или опыт в IT/смежной сфере (тестирование, разработка и т.д.); – базовое понимание SQL и BPMN или желание разобраться;

- Гражданство и локация РФ. - Опыт системного анализа от 5 лет, из них 2-3 года на лидирующих позициях.

⭐️Проект - Оптимизация бизнес-процессов в логистике на базе SAP-ландшафта клиента в крпунешем ритейлере Узбекистана. 🔸5+ лет опыта в роли бизнес-аналитика или процессного аналитика или аналитика по эффективности.

Готовить и поддерживать ежедневную, еженедельную и ежемесячную операционную отчётность для руководителей и функциональных команд. Разрабатывать и автоматизировать отчёты в Excel с использованием Power Query, сложных формул и макросов.

В крупном коммерческом банке требуется системный аналитик уровня Senior для участия в разработке интеграционных решений. Кандидат должен иметь опыт работы в сфере FinTech и владеть навыками системного анализа. Работа предполагает удалённый формат с возможностью сотрудничества на международном уровне.

Компания HelioCore, работающая в сфере HRTech, расширяет команду и приглашает на позиции Business Analyst и HR Analyst уровня Middle и Senior. Вакансии предполагают работу в офисе в Варшаве, Польша. Основные задачи включают анализ данных и процессов в области HR, выявление возможностей для оптимизации и внедрение решений с использованием искусственного интеллекта и автоматизации.

As we continue to scale and build the next generation of high-performance platform technologies, we’re looking for a Data Analyst to join our mission and turn complex player, operational, and financial data into actionable strategic insights. - Гипотезы и рекомендации для продуктовых и бизнес-команд.