Data Scientist вакансии - в офисе

TBC Uzbekistan is a digital financial services ecosystem powering some of Uzbekistan's most innovative financial experiences. Languages required: English (C1), Russian (B2). Work format: On-site in Tashkent, Uzbekistan.

разбираться в сложных моделях (от классики до нейросетей). погружаться в бизнес-процессы, проводить независимую оценку качества моделей.

Разработкой AI агентов на основе современных генеративных сеток (GigaChat и OpenSource); Разработкой системы офлайн- и онлайн-метрик для оценки качества LLM-приложений;

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

глубокий анализ данных для построения и калибровки моделей. опыт построения uplift-моделей и кривых отклика для оптимизации процентных ставок/Разработка моделей оценки эластичности спроса на кредит по ставке.

Компания Mayflower — технологическая компания, создающая высоконагруженные продукты, которыми пользуются миллионы людей по всему миру. Условия: — Офис в Лимасоле, Кипр — Помощь с переездом

Задачи: — Разработка и внедрение ML-моделей прогнозирования спроса (временные ряды, градиентный бустинг, нейросети) на уровне SKU; — Автоматизация пайплайнов ретрайна и мониторинга качества прогнозов в продакшене с использованием MLflow, Airflow, Docker; — Интеграция внешних факторов (промо, погода, сезонность, макроэкономика) и сценарное "what-if" моделирование; — Согласование иерархических прогнозов для оптимизации запасов в цепочке поставок (дефицит ↔ перепроизводство). Требования: — Уверенное владение Python (Pandas, NumPy, Scikit-learn); — Продвинутый уровень SQL (оконные функции, CTE, оптимизация запросов); — Опыт работы с Big Data: PySpark (распределённые данные > 100 ГБ); — Знания MLOps: Airflow / Prefect, MLflow / Weights & Biases, Docker, CI/CD; — Опыт работы с облачными платформами: AWS (SageMaker, S3, Redshift) или Azure / Yandex Cloud; — Знания статистики: теория вероятностей, проверка гипотез, A/B-тестирование, байесовские методы; — Бизнес-ориентация: понимание метрик (WMAPE, Fill Rate, оборачиваемость) и их влияния на P&L; — Умение объяснять сложные вещи простым языком нетехническим менеджерам и закупщикам; — Проактивность: самостоятельный поиск гипотез и точек роста, а не просто выполнение ТЗ; — Стрессоустойчивость: работа с "грязными" данными и умение отстаивать качество прогнозов перед бизнесом. Условия: — Работа в офисе у м. Кунцевская, бизнес-центр Kutuzoff Tower; — График работы 5/2 с 09:00 до 18:00; — Официальное оформление по ТК РФ, официальная зарплата с больничными и отпусками; — Заработная плата обсуждается на собеседовании; — Корпоративные скидки на продукцию компании (алкоголь); — Подарки сотрудникам и их детям на праздники.

Задачи: — глубокий анализ данных для построения и калибровки моделей — опыт построения uplift-моделей и кривых отклика для оптимизации процентных ставок — разработка моделей оценки эластичности спроса на кредит по ставке — разработка, обучение и валидация продвинутых ML-моделей для индивидуального ценообразования (регрессионные модели, градиентный бустинг, методы обучения с подкреплением / Reinforcement Learning для динамического ценообразования) — анализ и интерпретация результатов A/B-тестов ценовых стратегий, оценка их бизнес-эффекта (влияние на выручку, маржу, конверсию, LTV) — построение и оптимизация симуляционных сред для предварительной оценки эффекта ценовых изменений. Требования: — глубокое знание и практический опыт (от 3 лет) применения методов машинного обучения и анализа данных для решения бизнес-задач, желательно в области ценообразования, экономики или финтеха — продвинутый уровень знаний в области статистики, эконометрики и методологии проведения A/B-тестов — понимание принципов микроэкономики и теории цен — опыт разработки и внедрения в production моделей, напрямую влияющих на финансовые метрики (выручка, маржа) — продвинутые знания SQL и Python для анализа данных и ML — опыт работы с Python ML-фреймворками (Scikit-Learn, LightGBM/CatBoost/XGBoost, PyTorch/TensorFlow, библиотеки для RL), Spark (PySpark), опыт работы с базами данных (GreenPlum, PostgreSQL, ClickHouse). Будет плюсом: — опыт построения систем динамического ценообразования (dynamic pricing) или моделей на основе обучения с подкреплением (Reinforcement Learning) — опыт работы в ритейле, финтехе, marketplace-сегменте или сфере услуг с высокой конкуренцией — умение переводить бизнес-задачи на язык данных и обратно — знание основ юнит-экономики и финансового моделирования — опыт разработки или интеграции решений с применением GenAI. Условия: — комфортный современный офис рядом с м. Кутузовская — офисный формат работы — ежегодный пересмотр зарплаты, квартальная и годовая премия — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте — расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа — гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ — подписка Прайм с возможностью совместного использования на трёх близких — вознаграждение за рекомендацию друзей в команду Сбера.

Задачи: — участвовать в разработке и валидации моделей кредитного скоринга — регулярно запускать и контролировать ML-пайплайны — мониторить производительность и стабильность моделей — проводить плановое переобучение моделей — готовить отчёты по производительности моделей, включая Gini / KS — поддерживать модели в продакшене Требования: — от 2 лет опыта в Data Science — понимание принципов кредитного скоринга — узбекский язык обязателен — опыт работы в банковской сфере или финтехе — уверенный SQL и Python, практический опыт с Apache Spark — опыт внедрения и сопровождения ML-моделей в продакшене — понимание MLOps и полного жизненного цикла ML-моделей Будет большим плюсом: — опыт работы в банках или финтех-компаниях Узбекистана Условия: — полная занятость — зарплата до 4000 USD на руки — Ташкент, работа из офиса — долгосрочный проект

Задачи: — Разрабатывать и внедрять ML-модели для прогнозирования поведения клиентов-физических лиц и розничного продуктового портфеля Банка — Рассчитывать ожидаемые денежные потоки по кредитным и пассивным продуктам Банка Требования: — Опыт работы в банковской сфере — Опыт разработки и внедрения ML-моделей от сырых данных до production — Работа с табличными данными — Свободное владение Python: Pandas, NumPy, Matplotlib (или аналогичными инструментами визуализации) — Знание SQL: оконные функции, сложные JOIN, оптимизация запросов к БД — Хорошее владение ML-библиотеками: CatBoost, LightGBM, XGBoost, Scikit-learn — Умение проводить стресс-тесты и А/В-тесты — Понимание особенностей розничных банковских бизнес-процессов — Преимуществом будет знание MLflow, Airflow, Butch Scoring, Docker, Spark / PySpark Условия: — Офисный формат работы — Офис в центре Москвы

Задачи: — разработка моделей по продуктам кредитов физических лиц (КФЛ) для Блока Финансы — модели досрочного погашения кредитов/вкладов ФЛ на портфельном/клиентском уровне — модели эластичности КФЛ — модели рефинансирования КФЛ — модели детекции аномалий по НОСТРО счетам Обязанности: — сбор и подготовка данных (Hadoop, pyspark) — тестирование различных гипотез — полный пайплайн разработки модели (бустинг, нейронные сети, временные ряды) — общение с заказчиками и Data Engineer Требования: — опыт работы Data Scientist от 2 лет — образование по направлениям "Математика", «Физика», «Мат. методы в экономике» — глубокие знания в области теории вероятностей, статистики — понимание алгоритмов ML-моделей (регрессии, классификации и т. д.) — владение Python (numpy, sklearn, pandas, matplotlib/seaborn/plotly, catboost/lightgbm/xgboost и др.) — будет плюсом владение одним из фреймворков DL, pyspark Условия: — комфортный современный офис рядом с м. Кутузовская — офисный формат работы — ежегодный пересмотр зарплаты, квартальная и годовая премия (зависит от должности и системы премирования) — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте (для вакансий уровня Junior) — расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа — гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ — подписка Прайм с возможностью совместного использования на трёх близких — вознаграждение за рекомендацию друзей в команду Сбера.

Задачи: — строить ML модели на основе различных данных: табличные, графовые, NLP, GEO, последовательности событий (кликстрим, транзакции и т.д.) — писать production код и выводить его в ПРОМ — улучшать процесс построения моделей, разрабатывать собственные прикладные модули командного фреймворка разработки для повышения производительности DS и улучшения качества кода в промышленной среде — выдвигать data-driven гипотезы/предложения для запуска новых механик, а также поиск инсайтов в данных. Требования: — глубокое знание классического ML — понимание основных Deep Learning подходов — продвинутое знание SQL — отличное знание Python — понимание и опыт АB-тестирования. Будет плюсом: — понимание специфики разработки ML-технологий, начиная с формулировки бизнес-проблемы, заканчивая выведением на регулярную основу в прод — опыт решения задач ранжирования или Real Time Bidding — опыт работы с Pyspark, Greenplum. Условия: — комфортный современный офис рядом с м. Кутузовская — офисный формат работы — ежегодный пересмотр зарплаты, квартальная и годовая премия — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте — расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа — гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ — подписка Прайм с возможностью совместного использования на трёх близких — вознаграждение за рекомендацию друзей в команду Сбера.

Рассматриваем и сильных выпускников с заметными достижениями - олимпиады.

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, 25M+ 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 This role owns how Higgsfield measures its product - from the first sign-up to whether people come back. You are the measurement owner for onboarding, first generation, the paywall, plans and credits, and retention. Product managers ship fast here - your job is to make sure they ship knowing. You make sure: - Every product decision has a number attached to it before it ships and after it ships - Experiments are designed to be readable, and then read honestly - including when the answer is “this didn’t work” - The events the product emits can be trusted; you own the definition, not just the query - A PM gets an answer in hours, not next sprint You are the person who decides what “it worked” means. WHAT YOU WILL DO Product measurement - Own the core product metrics: activation, first-generation success, generation depth, free→paid conversion, repeat usage, retention by cohort and segment. - Define each metric once, write the definition down, and hold the line on it - one definition across dashboards, decks, and Slack. - Instrument new features before launch, with product and engineering: which events, which properties, what success looks like, when we call it. - Watch the health of the event stream itself - tracking drift, double counting, missing parameters, naming breakage and find the problem before a decision gets made on top of it. Experimentation - Design A/B tests properly: hypothesis, one primary metric, unit of randomization, MDE, sample size, run length, guardrail metrics. - Read them honestly: significance, peeking, novelty effects, sample-ratio mismatch, segment heterogeneity. - Separate “we proved this works,” “we proved this doesn’t,” and “we can’t tell from this test” - and say which one out loud. Product & monetization analysis - In-product funnel work: onboarding and quiz, first generation, paywall, checkout, plan choice, credit top-ups. - Pricing and packaging analysis: plan mix, credit consumption, unit economics per generation, margin by feature and by model. - Feature adoption and its real effect on retention and revenue — separating “users who do X retain better” from “making people do X improves retention.” - Behavioural segmentation: casual creators vs corporate/B2B, new vs returning, by model and by use case. Mixing them hides everything that matters. Making it usable - Build the few dashboards PMs actually open on their own — decision-shaped, not comprehensive. - Write short readouts a non-analyst can act on. Visualize data for humans, not for other analysts. - Automate recurring reporting so your hours go to new questions, not refreshes. - Work in SQL, Python, BigQuery and product analytics tools. We use AI heavily - resourcefulness beats syntax. WHO WE'RE LOOKING FOR We're looking for an analyst with opinions. You should have: - Strong SQL: window functions, cohorts, funnels and retention on raw event data, without help. - Python at working level for analysis (pandas, notebooks). Entry level is fine - we use AI heavily. - Real experimentation experience: you have designed tests, run them, and killed features with the results. - Statistical honesty: you know what a p-value does and does not entitle you to say, and you have refused to call a flat test a win. - Understanding of subscription + one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, deferred value of unspent credits. - The instinct to check the instrument before explaining the movement - a surprising number is a claim, not a fact. - Ownership: you decide what's worth analyzing, you chase the fix, you follow the recommendation to a shipped change. - Clear written and spoken English, B2+. Backgrounds that often do well: - Product analysts from consumer subscription or PLG products - Growth/BI analysts who moved into product and stayed - Early-startup analysts who built product measurement from zero - Data scientists who got tired of models and want decisions WHAT THIS ROLE IS NOT This role is not a fit if you: - Wait for a ticket to tell you what to analyze - Want to build models more than you want to change decisions - Need a data engineering team and clean tables to exist before you can start - Would report a lift you don't believe in because a stakeholder wants it - Need perfect data before you can say anything useful - 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) WHAT WE OFFER - Competitive base salary in USD, based on your experience, skills, and the scope of the role. - Equity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth. - Relocation support to Almaty for candidates moving from another city or country. - A highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company. - Opportunities for professional growth, ownership, and career development as the company scales. - Company-provided equipment, meals, transportation, or other office benefits.

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, 25M+ 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 Right now we treat 25M+ people as one user. The same paywall, the same discount, the same lifecycle email, the same retention effort. And we mostly conclude that a campaign worked by looking at what the people we sent it to did next — which is not the same thing. This role makes the company act per user, and know what that action was worth. You own both halves of that: the models that decide who we aim at, and the causal reads that say whether it landed. Who is about to leave. Who will come back on their own without us spending anything. Whose decision a discount actually changes. And what a payer is worth over their life, once the discount, the refunds and the compute they consume are all inside the number. You make sure: - Discounts and credit vouchers go to the people whose behaviour they change, and nowhere else - We know who is about to churn early enough to do something about it — and we know whether the something worked - Lifetime value is a number we can defend by cohort, by plan and by the discount a cohort was acquired on, not one blended average that hides all three - Every model and every campaign has a holdout behind it, so we can always state what it is worth in money WHAT YOU WILL DO Churn, retention & lifecycle propensity - Build churn and downgrade prediction for subscribers, and repeat-purchase propensity for one-time credit-pack buyers — two different machines, not one model with a flag on it. - Know the difference between a model that predicts churn and a model that reduces it. AUC earned by detecting users who already stopped using the product is worth nothing. - Hunt leakage relentlessly. Cancellation-adjacent features will hand you a beautiful offline number and a useless system. - Pair every score with an intervention and a holdout. The deliverable is a retained user, not a ranked list. Uplift modelling & offer targeting - Own incrementality on everything we aim at a user: discounts, credit vouchers, trials, upgrade prompts, win-back campaigns, retention saves. - Model uplift, not propensity.Targeting the users most likely to convert spends margin on people who were going to convert anyway; the entire value of this work is finding the users whose decision the offer changes. - Design the randomized holdouts that make uplift trainable and measurable in the first place, and keep them running permanently — including the ones marketing will ask you to switch off. - Respect the guardrails. An offer model optimizing conversion alone will happily find the accounts that convert at negative gross margin, and it will find the abuse rings first. Margin and abuse signals are constraints on the objective, not a later cleanup. - Work with Legal on what may be personalized. Personalized pricing and discounting touches consumer-protection and consent rules in several of our markets — you should want that conversation, not route around it. Lifetime value & payer economics - Own LTV: cohort-based, censored honestly, stated on contribution margin rather than gross revenue, and split by plan, segment and acquisition discount. - Model the parts that actually move it — refunds and chargebacks, credit breakage and expiry, plan migration and downgrades, and the compute an unlimited plan consumes. - Make it a decision input, not a slide: what we can pay for a user, which discount depth pays back, which plan we should stop selling. - Keep it current and versioned. A stale LTV curve is worse than no curve, because people act on it. Experimentation & causal measurement - Design and read the experiments behind pricing, packaging, offers and lifecycle — with the product analytics team, not in parallel to it. - Reach for the right tool when a clean experiment is impossible: difference-in-differences, matching, switchback and geo tests, synthetic control — and name the assumption each one is buying. - Separate "users who do X retain better" from "making users do X improves retention." Every time, out loud. - Kill your own results. A finding that doesn't survive a second look should die at your hands, not in a board deck. How we ship - Own the work end to end: framing the decision, features, training, evaluation, the scored audience landing in the tool that sends the offer, monitoring, retraining. - Models ship as batch scores and policy rules into lifecycle tooling, the paywall and pricing — plus the readout that says what they earned. There is no real-time serving stack to babysit. - Monitor for drift. New model launches, price changes, discount campaigns and seasonality all move the ground under a deployed score. - Write down what each model is worth, in money, and keep that number current. WHO WE'RE LOOKING FOR - Experience shipping models that changed what the business did to real users— a targeted campaign, a pricing rule, a retention programme. Notebooks and offline benchmarks are not this. - Depth in at least two of: churn and propensity modelling, uplift & causal ML, LTV and subscription economics, experimentation at scale. - Genuine causal literacy: randomized holdouts, incrementality, Qini and uplift curves, selection effects, and why a lift measured before-and-after is usually not a lift. - Strong SQL and Python: cohorts, funnels and retention on raw event data without help; gradient boosting; and the boring parts — feature pipelines, retraining cadence, a scoring job that runs on a schedule and doesn't rot. - Understanding of subscription plus one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, the deferred value of unspent credits, and why revenue and margin disagree about the same customer. - Commercial judgment: you name the decision and the metric before you pick the model, and you know when the answer is a rule rather than a model. - Pragmatism. A segment rule shipped this month that lifts retention beats a two-quarter uplift platform. Then you replace the rule. - Clear written and spoken English, B2+. Backgrounds that often do well: - Growth, retention or monetization data scientists from subscription, gaming, fintech or e-commerce — anywhere offers and churn are modelled with real money attached - CRM and lifecycle data scientists who owned campaign targetingandits incrementality - Causal inference and uplift specialists who ship policies rather than papers - Strong product analysts who moved into modelling and kept the decision instinct WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to train or fine-tune generative video models — that's what our R&D ML Engineer roles do, and they're open - Want to own a recommender or a serving stack — ranking is not in this role's scope today - Would target an offer at the users most likely to buy and call it personalization - Report a lift from a before-and-after comparison, or from comparing the users you targeted to everyone you didn't - Optimize AUC and hand the score to someone else to work out what to do with it - Need clean labelled data, a feature store and an experimentation platform to exist before you can start - Want predictable 9–5 workdays HIRING PROCESS We move fast: - Screening call (30 min) - Technical interview & case study (1 hour) - Practical home task (7 days) - Team interview (60 min) - Paid on-site trial (1 month) WHAT WE OFFER - Competitive base salary in USD, based on your experience, skills, and the scope of the role. - Equity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth. - Relocation support to Almaty for candidates moving from another city or country. - A highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company. - Opportunities for professional growth, ownership, and career development as the company scales. - Company-provided equipment, meals, transportation, or other office benefits.

Задачи: - разрабатывать надежные backend-сервисы для AI-помощников на основе GigaChat - создавать и поддерживать системы сбора и обработки данных: парсинг, логирование, аналитика - оптимизировать код, повышать стабильность и производительность сервисов под высокой нагрузкой - применять инструменты Data Science для улучшения работы систем и интеграции LLM в реальные бизнес-процессы. Требования: - рассматриваем студентов ВУЗа технической специальности, бакалавр 3-4 курс, магистратура 1-2 курс - уверенно владеете Python - знаете библиотеки FastAPI, aiohttp, SQLAlchemy, pandas, bs4 - имеете навыки асинхронного программирования - разбираетесь в SQL и ORM - понимаете принципы REST API, а также основы объектно-ориентированного проектирования (ООП, SOLID) - работаете с Git.

Location: Paris Workplace: OnSite ABOUT VOODOO Voodoo is a consumer tech company founded in Paris in 2013, with one mission: entertain the world. Today we are 1,000 people, generating $1b+ in annual revenue, profitably. Our games and apps have passed 7 billion downloads and reach 200 million+ active users every month, making Voodoo the #3 mobile publisher worldwide in downloads, after Google and Meta. We were backed by Goldman Sachs, Tencent and GBL along the way. TEAM Blitz is a gaming platform launched by Voodoo in 2021, allowing players to compete on mobile games for real cash prizes (head-up, tournaments, leagues, etc.). It is leveraging Voodoo's unique game portfolio to engage users in a fun and rewarding way. ROLE This role sits at the intersection of the Data and Gaming teams. You'll be the analytical partner to game designers and product managers, owning the measurement and tuning of the games themselves — from difficulty and game parameters to matchmaking fairness and the in-game economy. Your work directly shapes what players experience and how the games perform. MISSIONS: - Own the A/B testing cycle for games and game parameters — designing experiments, validating changes, and reading out results that drive core KPIs (engagement, RTP, rake, fairness, retention). - Analyze and calibrate game parameters — difficulty, scoring, completion rates, seed selection, and payout structures — to hit target player experience and economic goals. - Translate analyses into clear, actionable recommendations that shape game design and the product roadmap. - Build simulations and prototypes to pressure-test parameter and economy changes before rollout, then monitor them live once shipped. - Collaborate closely with game designers and engineers to embed data-driven decisions across the full game lifecycle. - Build and maintain dashboards and reports that keep the Gaming and Data teams aligned on critical game and economy metrics. PROFILE - 3+ years in analytical roles, ideally within gaming or a data-intensive product environment, comfortable working directly with product and design teams. - Skilled in SQL and Python; experience with large, complex datasets. - Strong grasp of A/B testing and statistical methods. - Comfortable with simulation and modeling to reason about game parameters, difficulty, and game economy outcomes. - Thrives in a fast-paced environment with rapid iteration. - Player-first mindset with genuine curiosity about games, player behavior, and game balance. BENEFITS - Competitive salary based on experience - Swile Lunch voucher - Gymlib (100% covered by Voodoo) - Premium healthcare coverage with SideCare, 100% covered for you and your family - Wellness activities in our Paris office - Remote days on Fridays