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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 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 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+. Backgrounds that often do well: - 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 This role is not a fit if you: - 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) - 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

Задачи: — Участвовать в формировании продуктовой стратегии data platform — Управлять ожиданиями стейкхолдеров и превращать потребности бизнеса в понятные data products, contracts и delivery plans — People Management: составлять планы развития DE, проводить performance review, 1:1, менторинг, дизайн команды и организация найма при необходимости — Работать со всеми стейколдерами команды (аналитики, dx, security, legal) над развитием data pipelines — Участвовать в развитии архитектуры платформы, модели данных — Управлять техническими решениями в продуктовых командах — Искать и реализовывать меры по оптимизации расходов на storage, compute и pipeline execution — При необходимости подключаться к сложным инициативам как staff data engineer Требования: — Опыт разработки по стеку: Python, Hadoop/Spark, Greenplum/Clickhouse/PostgreSQl, Yandex cloud/GCP/AWS/Azure, Kafka/RabbitMQ — Опыт руководства data engineering или платформенной командой от 5 человек — Понимание data platform architecture, data contracts, lineage, monitoring, reliability — Опыт управления качеством данных и операционными метриками — Сильная коммуникация с бизнесом и смежными командами — Опыт найма, развития и оценки инженеров

Responsibilities: — Design, create, and maintain automated test scripts (UI & API). — Integrate test automation suites into CI/CD pipelines. — Analyze test results, investigate failures, and report bugs clearly. — Work closely with developers and manual QA engineers to expand automation coverage. Key Requirements: — 3+ years of hands-on experience in QA Automation. — Proficiency in Python or JavaScript / TypeScript (or Java). — Strong experience with UI automation frameworks (Selenium, Playwright, or Cypress). — Hands-on experience with API testing and automation (Postman, REST Assured, or requests). — Understanding of basic testing concepts, methodology, and software development lifecycle (SDLC). — Familiarity with Git version control. — Basic understanding of CI/CD tools (GitHub Actions, GitLab CI, or Jenkins). Conditions: — Location: Remote within EU (must hold EU Citizenship, PR, or a valid EU Work/Residence Permit). — Type: Full-time / B2B Contract. — Fluent English (B2+/C1) is required.

Описание проекта: создание экосистемной Customer720 платформы. Данные из банковских систем и CRM других бизнесов. Задачи: — Владеть архитектурой и развитием Data Core. — Проектировать слои Bronze / Silver / Gold и модели данных. — Исследовать банковские, legacy- и внешние источники, восстанавливать их семантику. — Строить pipelines от raw-данных до подготовленных data products. — Разрабатывать трансформации на SQL / dbt / Python. — Определять инженерные стандарты для ELT, data modelling, metadata, lineage и data quality. — Развивать data catalog и metadata-as-code подход. — Проектировать data contracts и интеграции Data Core с другими компонентами. — Переводить исследовательские решения и ручные процессы в production. — Работать с Git, CI/CD, Docker, Linux и закрытыми Dev/UAT-контурами. Требования: — Совокупный релевантный опыт не менее 6 лет обязательно. — Банковские или fintech-данные. — Core banking / legacy banking systems. — Опыт работы Lead / Staff / Principal Data Engineer или сильного Senior с архитектурным ownership. — Опыт проектирования и самостоятельной реализации data platform / DWH / Lakehouse. — Customer 360 / Golden Record / MDM. — Сильный SQL и уверенный Python. — dbt или аналогичные transformation frameworks. — Airflow / Dagster / Prefect или аналоги. — Хорошее понимание data modelling, historization, grain и master entities. — Практический опыт data quality, lineage и metadata management. — Data Vault 2.0. — OpenMetadata или другие data catalog solutions. — Умение самостоятельно принимать технические решения и отвечать за результат. — Опыт работы с плохо документированными источниками. — Git, CI/CD, Docker, Linux. — Практический AI-native способ разработки. Условия: — Оформление: ИП, ГПХ. — Формат работы: полная занятость.

Задачи: — Полное ведение приёмки новых версий моделей: определение необходимых замеров, запуск оценок, объединение результатов и подготовка заключения о качестве модели. — Развитие системы метрик для оценки реального качества моделей: определение обязательных сигналов, объединение разных оценок, расчет статистической значимости, отличение настоящих изменений от случайного шума. — Работа с данными и инструментами приёмки: определение состава оценочных наборов, контроль покрытия важных сценариев, управление версиями, обеспечение воспроизводимости замеров. — Развитие инструментов: создание автоматических оценщиков на базе LLM (LLM-as-a-judge), применение гибридных методов. — Управление командой и процессами: формирование команды аналитиков приёмки, распределение направлений работы, наставничество, планирование ресурсов. Требования: — Опыт управления командой аналитиков не менее двух лет. — Умение работать с данными и извлекать из них практический результат. — Знание математической статистики и теории вероятностей. — Навыки программирования на Python и знание алгоритмов. — Умение формулировать обоснованные заключения и защищать их перед заказчиками в условиях неполных данных. Условия: — Ежегодные медицинские чекапы, йога и психотерапия. — Другие бонусы, полный список которых доступен по ссылке https://yandex.ru/jobs/pages/benefits.

Требования: — Опыт работы с сетями в ЦОД; — Экспертное знание и практический опыт работы с сетевыми протоколами, применяемыми в ЦОДах (MCLAG, BGP, OSPF, VxLAN и пр.) и офисах; — Опыт управления командой от 3-4 человек; — Опыт подбора и мотивации сотрудников; — Понимание устройства современных компьютеров и операционных систем в целом; — Опыт работы с linux, с средствами версионирования, управления задачами и дефектами и иной инфраструктурой разработки ПО; — Хорошее понимание процессов разработки и тестирования; — Организаторские способности, системное мышление, аналитические навыки, внимание к деталям. Будет плюсом: — Опыт разработки сетевой архитектуры/реализации проектов ее внедрения; — Опыт работы с автоматизацией тестов на уровне кода (Python, Go и т.п.); — Курсы/сертификация Cisco CCNA/Juniper JNCIA/etc., Huawei Certified ICT Professional/Expert (HCIP/HCIE) for Datacom, Ericsson Extreme; — Опыт тестирования комплексных высоконагруженных систем. Обязанности: — Усиление имеющейся команды тестирования сетевого оборудования, руководство выделенной командой; — Анализ, ревью требований и сценариев использования, подготовка дизайна тестирования и тестовых сценариев; — Анализ и оптимизация тестовой базы, улучшение тестового покрытия; — Организация и проведение тестирования по новой функциональности и приемочного для релизов; — Подготовка планов тестирования, координация их выполнения членами команды, непосредственное участие в выполнении тестирования; — Взаимодействием со смежными командами для координации работ и устранения препятствий в работе команды; — Анализ результатов тестирования, описание и классификация найденных проблем, подготовка отчетов; — Совершенствование процессов и методов тестирования; — Измерение и улучшение качества продукта.

Задачи: — Развитие моделей поискового ранжирования и рекомендательных систем. — Тюнинг моделей и feature engineering. — Разработка новых ML-решений с нуля. — Вывод моделей в production. — Анализ A/B-тестов и результатов экспериментов. Требования: — 6+ лет опыта. — Опыт внедрения ML-моделей в production. — Знание ML и нейросетей для обработки текста. — Опыт в ranking, recommendations или NLP. — Уверенный Python: NumPy, SciPy, PyTorch, Scikit-learn. — Знание алгоритмов и структур данных. — SQL и опыт работы с большими данными. — Понимание A/B-экспериментов. Будет плюсом: — Java. — Cassandra, Hive, Presto, Kafka. — Hadoop. — LightGBM. Условия: — Полная занятость. — График 5/2, 8 часов в день. — Удалённая работа или офис. — Гибкий график. — Оформление по ТК РФ. — «Белая» зарплата. — ДМС с первого месяца. — Обучение и конференции за счёт компании. — Well-being программа. — Офисы в 10 городах России. Основные задачи проекта: — Развитие поиска и рекомендаций. — Улучшение продуктовых метрик через ML-модели. — Работа с высоконагруженным поисковым кластером. — Полный цикл ML: данные → модель → production → A/B-тест. О компании: — Крупный технический департамент. — Около 600 специалистов в IT, Data и Product. — 60+ команд. — ML-продукты используются миллионами пользователей.

Location: Paris, France Workplace: Hybrid HEALTH CAN’T WAIT. Not for symptoms to get worse. Not for a six‑month appointment. Not for a system to catch up. But that’s exactly how healthcare works today. You wait, until you can’t. Alan exists to end the wait. Health is a universal right, and we believe this right can only become real when it’s coupled with prevention. We need to stop treating health as something we repair and start treating it as something we build, every day. It’s not solely a question of willpower. It’s the healthcare system itself that needs to work for everyone, in a sustainable way. So we are building the new standard in prevention insurance. Alan is the first company that integrates insurance, prevention, and care into a single, acclaimed user experience. We are on an incredible journey to build a global leading company, with a unique culture https://alan.com/en/careers. We already partner with 40K+ companies of all sizes, serving more than 1M+ members, and have reached €800M+ in ARR. Prevention as the new norm. That's what we're building with our team of 1000+ people. If it speaks to you: we're hiring across France, Spain, Belgium, and Canada. And beyond. MISSION Alan is scaling internationally (800+ employees, 100+ healthcare professionals, 5+ countries) and needs IT Engineers to build and own the internal technology platform every Alaner works on: identity and provisioning, endpoint and SaaS security, the tooling behind our clinics, and the automation that keeps IT from scaling linearly with headcount. The scope below is what the team owns, not a checklist for one person. You'll be staffed on one or two of these areas at a time, according to your strengths and interests, and it will evolve through time based on company needs and personal appetite. Staying close to users keeps us building the right things: you'll spend up to 2-3 days a month on IT support, seeing first-hand where friction lives and what's worth automating next. SCOPE ⚙️ PLATFORM & AUTOMATION - Own and extend automations on Alan Home, our internal provisioning engine - Maintain, extend and improve helpdesk automation, SaaS provisioning flows, and onboarding/offboarding pipelines - Own the meeting room AV and room-booking platform as a product, replicable across offices 🔑 IDENTITY & ACCESS - Own the IT side of the employee lifecycle (onboarding, offboarding, mobility between jobs and countries) across Google Workspace, SSO/SAML and our SaaS ecosystem. - Grow our role-based access model: extend it to more tools and refine the permission groups, so access is granted by role rather than case by case - Make employee lifecycle events reliable, fast and auditable end-to-end - Own access reviews and least-privilege models; produce and maintain the evidence our auditors rely on - Own SaaS procurement and license management: vendor relationships, renewals, and cost optimization across our stack 🔒 ENDPOINT & SAAS SECURITY - Own the Mac fleet in Kandji: enrollment at scale, role-based blueprints, fleet compliance - Cloudflare WARP, SaaS security controls - Maintain and strengthen our ISO 27001 certification and drive ACPR and pentest remediation on the endpoint and platform scope 🩺 HEALTH PROFESSIONALS IT - The IT layer behind Alan's clinics: practitioner tooling, equipment, access - Role and permission automation for healthcare professionals, and their support and escalation process 🌍 INTERNATIONALISATION - Make international expansion something IT accelerates, not something it slows down: a repeatable process where every new market (Canada, KSA, Armenia...) launches on our standard stack from day one - Bring acquired entities onto that same platform MILESTONES First 3 months - Acquire technical knowledge on Alan's platform: Alan Home, Google Workspace/SSO, Kandji, Cloudflare WARP - Take one project end-to-end and ship a first increment - Join the IT oncall team as technical escalation First 6 months - Own a workstream end-to-end - Deliver one automation or hardening initiative end-to-end - Take up documentation and automation on your scope and reduce significantly the bus factor on it - Mentor IT teammates on your core expertise First year - Deliver measurable IT toil reduction on your scope - Set the technical standards on your domain: how we build, document and review it - Build strong cross-functional partnerships (Engineering, Security, Workplace, Clinic, People, Audit) - Contribute to hiring and onboarding new teammates 3 years - Lead major projects end-to-end across several communities - Be the reference the team relies on across at least two domains - Mentor the IT team and beyond, and scale IT without a linear increase in manual work As the community grows, there is room to take on more responsibility: leading a crew, owning cross-community projects, mentoring, hiring. WHAT WE'RE LOOKING FOR Must-have - Technical/hard skills: - 5+ years in IT engineering, platform, systems or corporate engineering, with real ownership of production-facing systems - Depth in at least one area (platform & automation, identity, endpoint & security) and demonstrated range beyond it. - You write code: not necessarily as a full-time software engineer, but Python, APIs and pull requests in a shared repo should feel normal - Structured problem-solving: diagnoses methodically, asks the right clarifying questions, thinks about failure modes and rollback - Comfort with regulated environments: health data, ACPR, ISO 27001, HDS - Automation & AI appetite: uses AI tools smartly to accelerate their own delivery, with an instinct for what should be automated or delegated to AI rather than repeated Must-have - Behavioral / soft skills: - Ownership & autonomy: drives topics forward without hand-holding, including when they cross into someone else's perimeter - Written communication: Alan is written-first; decisions get argued in public documents and challenged by people outside your team - Pragmatic judgment: balances "perfect" vs. "good enough", decides with incomplete information - Documentation culture: reduces bus factor through clear documentation and knowledge sharing Nice-to-have skills / Bonus points if you have: - Experience with an internal provisioning or IdP-driven automation platform - Kandji or another MDM at scale - Cloudflare Zero Trust / WARP, Datadog - Audit remediation experience (ISO 27001, HDS) - Multi-country rollouts, or IT integration after an acquisition - Having mentored, coached or led a (small) crew - Health or healthtech ecosystem familiarity Languages: Fluent English & Professional French

Задачи: — Анализ защищенности внутренних и внешних сервисов компании с использованием методики OWASP WSTG (black/gray/white box тестирования); — Подготовка рекомендаций (написание отчетов) по повышению уровня защищенности по результатам проведенных работ; — Участие в проектах по тестированию на проникновение и RedTeam в качестве специалиста по разработке и поддержке инструментария, используемого коллегами; — Исследовательская деятельность в области информационной безопасности, тестирование PoC-ов, развитие собственных компетенций. Требования: — Опыт работы/практика в области практического анализа защищенности веб-приложений; — Опыт работы/практика в разработке и автоматизации на языках Python (обязательно), Bash, Ansible, Terraform (желательно), Go, C# и других (дополнительно); — Владение основными инструментами анализа защищенности веб-приложений (в частности, BurpSuite Pro с различными плагинами); — Работа с эксплойтами, их модификация и выполнение; — Знание методик анализа защищенности (PTES, MITRE ATT&CK, OWASP WSTG); — Знание используемых в корпоративной cреде систем и технологий и атаки на них; — Знание сетевых протоколов и протоколов аутентификации и авторизации различных систем; — Умение читать и анализировать исходный код на распространенных языках программирования; — Технический английский.

Задачи: — Отвечать за поиск и анализ причин оттока / цементирования клиента в базе — Оценивать эффект проектов, связанных со снижением оттока: потенциальные эффекты и постанализ после запуска — Создавать инструменты (дашборды/отчеты) для отслеживания KPI Требования: — Аналитическое мышление: способность структурировать информацию и выявлять закономерности — Готовность работать 5/2 в офисе в Москве (в других регионах возможен гибрид или удаленка) — Работа с данными: уверенное владение SQL — Визуализация: опыт работы с Power BI, Tableau или аналогичными инструментами — Статистический анализ: базовые знания статистики и методов анализа данных — Программирование: базовые знания Python для обработки данных (желательно) — Метрики: понимание основных продуктовых метрик и их взаимосвязей — Документация: умение составлять технические задания и аналитические отчеты — Коммуникабельность: умение четко доносить мысли до команды и стейкхолдеров — Внимание к деталям: способность замечать мелкие, но важные аспекты — Самоорганизация: умение планировать работу и соблюдать дедлайны — Проактивность: инициативность в поиске решений и улучшений — Критическое мышление: способность анализировать информацию и делать обоснованные выводы — Стрессоустойчивость: работа в условиях многозадачности и сжатых сроков — Обучаемость: готовность изучать новые инструменты и методологии

Требования: — 5+ лет опыта в DevOps / SRE / инфраструктуре — Опыт работы с Kubernetes (K8S) — Опыт с Bastion Host / Operations Audit — Навыки устранения проблем с базами данных: SQL Server, репликация, блокировки/взаимоблокировки, большие таблицы — Навыки программирования/скриптинга на Go или Python — Знание Zero Trust Security — Опыт работы с системами наблюдения, мониторинга, логирования и трассировки — Опыт поддержки высоконагруженных систем и обработки DDoS/CC атак — Опыт работы с Redis, Kafka, Doris и .NET средами — Сильные навыки оптимизации архитектуры и контроля затрат на инфраструктуру — Свободное владение китайским и английским языками

Задачи: – Развивать платформу данных; – Разрабатывать и оптимизировать ETL/ELT-процессы; – Работать с batch- и streaming-пайплайнами; – Развивать модели данных и хранилище; – Повышать производительность и надежность платформы; – Настраивать мониторинг, алертинг и контроль качества данных; – Автоматизировать эксплуатационные процессы; – Участвовать в технических ревью и развитии инженерных практик команды. Требования: – Уверенное владение Python и SQL; – Опыт работы с Spark, Clickhouse, Hadoop, Kafka, Greenplum, S3 (Ceph, Minio); – Опыт написания DAG-ов для Airflow; – Опыт работы с Docker и K8S; – Навыки работы с Linux; – Навыки работы с Git.

Задачи: — Разработка ядра системы перемещения товаров: проектировать и внедрять комплекс сервисов, от которых зависит выполнение заказов. Важны надёжность, предсказуемость и устойчивость системы под нагрузкой. — Разработка интеграционной платформы для взаимодействия со складскими системами партнёров: создавать унифицированную платформу, которая глубоко встроена во внутреннюю инфраструктуру и одновременно служит точкой интеграции с внешними партнёрами, а также обеспечивает прозрачность процесса доставки. — Реализация приложений для водителя на мобильных платформах: разрабатывать приложение, которое будет простым в освоении для пользователей и в то же время высоконадёжным. Оно должно сохранять целостность данных при транзакциях в условиях нестабильного соединения, безошибочно обрабатывать очереди операций и обеспечивать корректность действий пользователей. Требования: — Проектировали высоконагруженные системы со строгими требованиями к надёжности, понимаете компромиссы консистентности и доступности. — Строили внешние интеграционные контуры и умеете фиксировать контракты. — Уверенно владеете Go, используете Python. — Пишете тестируемый код. Будет плюсом, если вы: — Работали в сфере логистики, WMS, TMS или e-commerce-учёта. — Имели дело с event-driven-архитектурами (Kafka или аналоги). — Разрабатывали бэкенд для мобильных клиентов (версионирование API, офлайн-режим). Условия: — ДМС с регулярными чекапами и вакцинацией. — Другие бонусы (полный список по ссылке).

Задачи: — Помогать в миграции существующего хранилища данных SAP BW на Snowflake, перестраивая трансформации с использованием SQL и dbt при сохранении бизнес-логики. Требования: — Практический опыт трансформации и миграции DWH. — Глубокие знания SAP BW: InfoCubes, DSOs, InfoObjects и трансформаций. — Продвинутый уровень SQL и моделирования измерений. — Предпочтение dbt. — Опыт оркестрации ELT с использованием ADF, Airflow или аналогичных инструментов. — Ответственное использование AI-инструментов в повседневной работе. — Преимуществом будут знания Snowflake, чтение ABAP-рутин, Python и макросов dbt. Условия: — Медицинская страховка. — Гибридный формат работы. — Поддержка благополучия и возможности обучения с карьерным коучем. — Зарплата от 2500 USD net в месяц.

Location: Москва В Департамент противодействия мошенничеству требуется Аналитик данных. С нами ты будешь анализировать самые актуальные тренды мошенничества в стране и мире, помогать принимать data-driven решения для защиты клиентов от мошенников, реализовывать самые свежие требования регулятора в этой сфере. Обязанности * выполнение ad - hoc запросов, визуализация и интерпретация данных, поиск инсайтов, выявление аномалий и закономерностей. Проведение аналитических исследований, связанных с противодействием кибермошенничеству * автоматизация и обеспечение расчёта ключевых показателей системы Антифрод * обновление и актуализация методологии по расчёту различных метрик, связанных с фродмониторингом * участие в проектах со смежными DS/DE- командами * сопровождение и формирование аналитических отчётов для руководства и кросс-функциональных команд * BI-аналитика: развитие дашбордов на платформе QlickSense и Навигатор. фыв Требования * Высшее техническое образование. * Опыт работы в области анализа данных от 1 года. * Уверенное знание SQL (включая оконные функции и вложенные запросы) * Уверенное владение Python для анализа данных (numpy, pandas, seaborn, mathplotlib) * Работа с большими данными является преимуществом (Spark, Hadoop). * Опыт работы с BI-платформами (QlickSense, PowerBI, Tableau) * Умение структурировать информацию, формулировать выводы и презентовать результаты; проявление инициативы, внимание к деталям Условия * комфортный современный офис рядом с м. Кутузовская, формат работы - офис * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Задачи: — разработка математической модели кейсов; — расчёт и настройка RTP, EV, House Edge; — расчёт вероятностей выпадения предметов; — variance / volatility и распределение выигрышей; — калибровка вероятностей под заданный RTP; — Monte-Carlo симуляции на 1–10+ млн открытий; — анализ экономики и прибыльности на большой дистанции; — оценка рисков и необходимого банкролла/резерва; — автоматический пересчёт модели при изменении рыночных цен CS2-скинов; — математическая проработка дополнительных игровых механик. Требования: — сильное понимание теории вероятностей и математической статистики; — опыт в iGaming, Game Math, Loot Boxes, Case Opening, RNG или игровой экономике; — будет плюсом: Python / NumPy; — опыт Monte-Carlo моделирования; — практический опыт работы с RTP и House Edge; — понимание экономики CS2-скинов; — примеры реализованных математических моделей. Условия: — долгосрочный проект; — после разработки базовой модели планируется постоянная работа над новыми режимами, механиками и балансом. При отклике нужно прислать кратко: опыт, примеры подобных проектов/расчётов, используемый стек и ориентировочную стоимость работы.