Данные и ML вакансии

• Development of machine learning and deep learning models. • Building solutions in NLP and Deep Learning.

построение моделей для управления розничным кредитным риском (PD, Expected Loss, прогнозирование событий жизненного цикла клиента). поддержание и изменение процессов калибровки и дообучения моделей.

Лидить команду DS (2 человека); Сопровождение ML-решений (мониторинг сервисов и моделей, отслеживание дрейфа данных, работа над развитием);

• Цикл ML-проекта. Вы будете преобразовывать бизнес-требования в ML-задачи, собирать пайплайн, обучать и проверять модель, внедрять её в реалтайм-сервис и тестировать с помощью A/B-теста.

Исследовать подходы к causal inference, uplift-моделированию и оценки эффекта изменения цены; Проектировать временную валидацию, backtesting и бизнес-метрики качества;

Location: Warsaw, Mazowieckie, Poland; Sofia City, Bulgaria; Limassol, Cyprus Workplace: hybrid We are a leading trading platform that is ambitiously expanding to the four corners of the globe. Our top-rated products have won prestigious industry awards for their cutting-edge technology and seamless client experience. We deliver only the best, so we are always in search of the best people to join our ever-growing talented team. As a Data Engineer , you will play a crucial role in our data team, contributing to developing and maintaining data pipelines and systems. You will work closely with data scientists, analysts, and other stakeholders to ensure the availability and reliability of data for decision-making and analytics. Your responsibilities will encompass data ingestion, transformation, and delivery, as well as maintaining and optimizing data infrastructure. The Data Engineer is expected to contribute to developing and maintaining data infrastructure, ensuring data reliability and availability. You should be able to work independently and as part of a team, adapting to changing data needs and collaborating with data professionals to provide valuable insights to the organization. Staying current with data engineering best practices and emerging technologies is essential for success in this role. Responsibilities: Design, develop, and maintain data pipelines that ingest process, and deliver data from various sources, ensuring data quality and reliability. Data Modeling: Create and maintain data models to support reporting, analytics, and business intelligence needs, optimizing data structures for performance and efficiency. Implement ETL processes to transform raw data into meaningful insights, handling data transformation, aggregation, and enrichment. Monitor and address data quality issues, implement data validation processes, and establish data governance practices. Manage and optimize data storage, processing, and distribution systems, ensuring scalability and performance. Collaborate with data scientists, analysts, and cross-functional teams to understand data requirements and deliver solutions that meet business needs. Document data engineering processes, pipelines, and systems to maintain clear and accessible knowledge for team members. Requirements: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. Minimum of 3 years of experience in data engineering or a related field. Proven experience with Redshift and Snowflake. Experience with DBT. Experience with Apache Airflow for workflow orchestration. Proficiency in Python for data pipeline development and scripting. Experience with AWS cloud services, including S3, EC2, and EMR. Familiarity with Kafka for real-time data streaming. Familiarity with data visualization and reporting tools (e.g. Tableau, Power BI or Looker). Project management skills using Jira or similar tools. Ability to collaborate effectively with cross-functional teams and understand business data needs. Strong problem-solving skills, a proactive approach to troubleshooting data issues, and critical thinking abilities. Adaptable and open to learning new technologies and methodologies. What you will get in return: • Competitive Salary: We believe great work deserves great pay! Your skills and talents will be rewarded with a salary that makes you feel valued and motivated. • Work-Life Harmony: Join a company that genuinely cares about you - because your life outside of work matters just as much as your time on the clock. #LI-Hybrid • Generous Time Off: Need a breather? Our annual leave policy lets you recharge and enjoy life outside of work without a worry. • Employee Referral Program: Love working here? Share the love! Bring your talented friends on board and get rewarded for growing our awesome team. • Comprehensive Health & Pension Benefits: From medical insurance to pension plans, we’ve got your back. Plus, location-specific benefits and perks! • Workation Wonderland: Live your digital nomad dreams with 30 extra days to work remotely from anywhere in the world (some restrictions apply). Adventure awaits! • Volunteer Days: Make a difference! Take two additional paid days each year to support causes you care about and give back to the community. Be a key player at the forefront of the digital assets movement, propelling your career to new heights! Join a dynamic and rapidly expanding company that values and rewards talent, initiative, and creativity. Work alongside one of the most brilliant teams in the industry.

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

-Участвовать в формировании продуктовой стратегии data platform. -Управлять ожиданиями стейкхолдеров и превращать потребности бизнеса в понятные data products, contracts и delivery plans.

* Владеть архитектурой и развитием Data Core. * Проектировать слои Bronze / Silver / Gold и модели данных.

— Развитие моделей поискового ранжирования и рекомендательных систем. — Тюнинг моделей и feature engineering.

– Развивать платформу данных; – Разрабатывать и оптимизировать ETL/ELT-процессы;

онлайн-интенсив 1 месяц; вы изучаете теорию и отрабатываете ее с помощью практических заданий;

Help migrate an existing SAP BW data warehouse to Snowflake, rebuilding transformations with SQL and dbt while preserving business logic. Hands-on DWH transformation and migration experience.

Задачи: — Развивать платформу данных; — Разрабатывать и оптимизировать ETL/ELT-процессы; — Работать с batch- и streaming-пайплайнами; — Развивать модели данных и хранилище.

⁃ Развивать Data Lakehouse и единое DWH. ⁃ Строить ETL/ELT-пайплайны от источника до готовой витрины.

Dive into complex systems and manual workflows to develop an understanding of our tools and constraints. Thoughtfully design and maintain clean, scalable, production-grade dbt models (e.g., ARR, revenue recognition).

Ищем AI/LLM Engineer, который будет проектировать и развивать production-системы на базе LLM. Ищем инженера, который умеет собрать LLM-систему целиком: от prompt/context logic и агентных сценариев до backend-сервисов, интеграций, тестирования и запуска в production.

Задачи: — Участие в операционной деятельности команды: дейли-митинги, спринт-планирования, ретроспективы, проведение демо для заказчика; — Контроль соблюдения сроков и качества результатов по всем активным проектам; — Проектирование и внедрение LLM решений: NER/классификация/обогащение, semantic search и RAG пайплайны, подготовка датасетов, fine tuning, улучшение метрик; — Ведение проектной документации в соответствии с принятыми стандартами и регламентами компании. Требования: — Опыт DS/ML (3+ лет) и общий опыт в IT около 6 лет, с фокусом на end-to-end внедрения AI/ML решений; — Опыт оценки трудозатрат для комплексных AI-проектов; — Инструменты управления: Jira, Confluence или аналоги для трекинга задач, ведения документации и управления бэклогом; — Понимание полного жизненного цикла ML-проекта: сбор данных, разметка, обучение, валидация, деплой, мониторинг; — Понимание принципов работы LLM, GenAI, RAG и агентных систем — на уровне, достаточном для оценки рисков; — Понимание принципов MLOps: CI/CD для моделей, версионирование данных, автоматизация пайплайнов; — Высокий уровень владения языком Python и опыт разработки production решений и интеграций с корпоративными системами; — Практика работы с корпоративными базами данных (PostgreSQL, ClickHouse, Vertica или аналогичными) и построением витрин/ETL (Airflow, Kafka и т.п.). Условия: — работа в аккредитованной IT-компании (льготная ипотека и проч.); — оформление по ТК РФ и его полное соблюдение (оплачиваемые отпуска, больничные); — ДМС со стоматологией с первых недель работы (а не после испытательного срока).