Вакансии

Задачи: — Администрировать, поддерживать и оптимизировать облачные платформы и инфраструктуру. — Настраивать, конфигурировать, поддерживать и совершенствовать CI/CD- и GitOps-пайплайны. — Внедрять и поддерживать практики Infrastructure as Code (IaC) с использованием таких инструментов, как Ansible, Terraform, Pulumi. — Управлять контейнеризированными средами с использованием Docker, Docker Swarm и Kubernetes. — Обеспечивать надежную инфраструктуру и процессы развертывания для BI-сервисов и приложений, включая ClickHouse, SQL, Airflow, Tableau, Superset. — Тесно взаимодействовать с командами разработки и BI для повышения надежности развертывания, консистентности окружений и операционной эффективности. Требования: — Уверенные навыки настройки и администрирования как минимум одной облачной платформы. — Уверенные навыки построения и автоматизации процессов развертывания с использованием CI/CD и GitOps. — Опыт построения масштабируемой IT-инфраструктуры. — Уверенные навыки настройки Linux-серверов и устранения неисправностей. — Практический опыт работы с Kubernetes. — Понимание сетевой модели OSI. — Понимание принципов построения надежной и масштабируемой инфраструктуры, а также автоматизированных процессов развертывания. — Знание принципов информационной безопасности и защиты данных. — Интерес к работе с инфраструктурой для Business Intelligence и data-related решений либо соответствующий практический опыт.

Requirements 2+ years of experience in Python/PyTest (for test scripting) At least 1+ years working on data-centric or analytics projects Experience with dbt, Snowflake Proficiency in SQL for querying and validating large datasets Exposure to Python or similar languages for test automation is a plus Experience validating ETL/ELT workflows or dashboards Exceptional attention to detail with outstanding analytical and problem-solving capabilities Ease in a cooperative, Agile setting Experience with JIRA and Confluence Experience with Git/GitHub English level B2+ Nice to have Experience with Power BI (read/test level), Azure Dev-Ops Airflow (read/debug level) Familiarity with test case management tools We offer Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota Competitive compensation that depends on your qualification and skills Career development system with clear skill qualifications Flexible working hours aligned to your schedule Options to work remotely Corporate medical insurance covering services of private and public medical centers English courses online Corporate parties and events for employees and their children Internal conferences, workshops and meetups for learning and experience sharing Gym membership compensation 5 days of paid sick leave per year with no obligation to submit a sick-leave certificate Any questions? Tatyana hh@itransition.com

Требования: — Коммерческий опыт в роли Data Engineer. — Хорошее владение Python или Scala / Java для написания кода обработки данных. — Отличные знания SQL (оконные функции, сложные объединения, оптимизация запросов). — Опыт работы с современными хранилищами данных (колоночные/реляционные/NoSQL БД) и инструментами обработки больших объемов данных. — Опыт автоматизации и управления процессами с помощью современных систем оркестрации (например, Airflow или аналоги). — Понимание принципов контейнеризации (Docker), работы с системами контроля версий (Git) и базовые навыки работы в Linux-окружении. — Аналитический склад ума, умение работать в команде, структурированность и проактивность.

Описание продукта: Один из ключевых юнитов в Big Data МТС - это платформа поиска и рекомендаций, которая использует ML и большие данные для подбора релевантных айтемов для клиентов из каталогов МТС. Поставляет рекомендации в онлайн-кинотеатр КИОН, читалку «Строки», МТС Банк, сервисы по продаже билетов, интернет-магазин и другие продукты экосистемы МТС. Задачи включают объединение рекомендательных движков в единую платформу, создание универсального поискового движка, realtime триггеров и масштабирование на все сервисы компании. Задачи: — Генерировать гипотезы по улучшению рекомендаций для роста бизнес-метрик клиентов — Превращать гипотезы в работающие ML модели — Катить модели в прод и по итогам АВ-тестов формулировать новые гипотезы по улучшению рекомендаций — Ставить задачи data инженерам на доработки витрин, взаимодействовать с разработчиками для формирования пайплайнов поставки рекомендаций, совместно с аналитиками сетапить АВ-тесты Требования: — Коммерческий опыт в data science от 2-х лет — Опыт разработки и продуктивизации ML-сервисов (от моделей до API) — Опыт внедрения ML-пайплайна в real-time сервисы — Опыт работы с docker, шедулерами (airflow или любой другой) и sql/nosql базами данных Условия: — Собственная платформа MTS Ocean для получения ИТ-ресурсов, деплой, мониторинг, observability — Профессиональные гильдии инженеров — Внутренняя площадка TechTalks для обмена опытом и развития навыков — Участие во внешних IT конференциях (HighLoad++, DataFest, Mobius, Test Driven Conf, Joker, DevOps, Матемаркетинг, собственная конференция True Tech Arch) — Курсы и вебинары в корпоративном университете и электронная библиотека — ДМС с первого месяца работы, включая стоматологию — Страхование от несчастных случаев с 1 месяца работы — Материальная помощь в сложных жизненных ситуациях — Отпуск 28 календарных дней — Прием врачей общей практики и массаж в офисе — Мобильная связь за счет компании и льготные тарифы для близких — Подписка на онлайн-кинотеатр KION, сервис МТС Музыка, книжный сервис Строки от МТС, безлимитные мессенджеры и соцсети.

Location: Los Angeles, USA At Riot, Data Engineers build the trusted data foundations that help teams make better decisions and create better experiences for players. As a Principal Data Engineer on Teamfight Tactics CoreTech , you will own the technical direction and long-term health of TFT’s data ecosystem, from telemetry and schemas to govern data products, metrics, pipelines, and production operations. You will partner closely with Game Engineering, Design, Product, Insights, and Data Science from feature conception through launch and live operations. You will translate player and product needs into scalable, reliable, and cost-efficient data solutions, while establishing the standards and operating practices that allow TFT teams to move quickly without compromising data trust. This is a Principal-level individual contributor role. You will lead ambiguous, multi-team initiatives; make architecture and prioritization tradeoffs; mentor engineers and data practitioners; and influence data-engineering standards across TFT and Riot. You will remain hands-on where it creates leverage, while raising the effectiveness of the broader team through technical direction, coaching, and durable systems. Responsibilities: Own the technical strategy, architecture, and roadmap for TFT’s data ecosystem in partnership with TFT and Riot Data Engineering leadership. Design, build, and evolve scalable batch and near-real-time data systems supporting telemetry, player behavior, game operations, experimentation, insights, and player-facing features. Establish and maintain data contracts across Data Engineering, Insights, Game Engineering, and Product, including event schemas, metric definitions, and accuracy expectations. Partner with game teams early in feature development to design instrumentation, validate payloads, and make telemetry a tested part of feature readiness and launch criteria. Own the reliability and operational health of critical TFT data products, including freshness, availability, accuracy, observability, alerting, runbooks, incident response, and root-cause analysis. Lead data modeling and warehouse architecture across TFT, creating durable, well-documented sources of truth that serve multiple teams and use cases. Establish standards for data governance, lineage, documentation, access, retention, lifecycle management, and compliance with applicable privacy requirements. Reduce ambiguity by clarifying requirements, identifying risks, evaluating alternatives, and communicating tradeoffs across delivery speed, reliability, cost, and complexity. Improve engineering velocity through automation, self-service tooling, reusable patterns, and pragmatic development and review processes. Drive cost optimization through thoughtful architecture, workload optimization, storage and compute lifecycle practices, and proactive capacity planning. Contribute to recruiting, technical interviewing, interview-kit development, and the growth of Riot’s Data Engineering discipline. Help define and socialize TFT and Riot-wide craft standards for data architecture, reliability, governance, documentation, and operational excellence. Required Qualifications: Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience. 8+ years of professional experience in data engineering, software engineering, analytics engineering, or a related discipline, with demonstrated principal-level scope. Experience designing, building, and operating production data systems at significant scale. Hands-on expertise in big-data technologies, including Python, GoLang, Spark, Scala, SQL, and tools like Airflow and DBT. Proficiency in cloud infrastructure (AWS or GCP) and Databricks. Demonstrated expertise in data modeling, schema design, data contracts, lineage, governance, and the development of durable sources of truth. Experience establishing data-quality checks, observability, SLIs/SLOs, incident-management practices, and operational documentation. Demonstrated ability to set technical direction, lead complex initiatives across multiple teams, and make sound architecture and prioritization tradeoffs. Strong cross-functional communication skills and experience partnering with engineering, product, analytics, insights, and other technical stakeholders. Experience mentoring and developing engineers, including senior-level practitioners. Desired Qualifications: Experience building and operating data systems for a live-service game or another high-scale consumer product. Experience partnering with game engineering on instrumentation, telemetry, schemas, and launch readiness. Experience using data and analytics to influence gameplay, product, feature, or live-operations decisions from development through launch. Familiarity with Teamfight Tactics or strong curiosity about games, player behavior, and live-service operations. Genuine player empathy, product sense, and pragmatic judgment in balancing player value, engineering quality, delivery, and operational risk. For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes the delight of players. We will be looking at your past studies, experience, and your personal relationship with games. If you embody player empathy and care about players' experiences, this could be your role! Our Perks: Riot focuses on work/life balance, shown by our open paid time off policy and other perks such as flexible work schedules. We offer medical, dental, and life insurance, parental leave for you, your spouse/domestic partner, and children, and a 401k with company match. Check out our benefits pages for more information. At Riot Games, we put players first . That mission drives every decision in our quest to create games and experiences that make it better to be a player. Whether you’re working directly on a new player-facing experience or you’re supporting the company as a whole, everyone at Riot is part of our mission. And just like in our games, we’re better when we work together. Our goal is to create collaborative teams where you are empowered to bring your unique perspective everyday. If that sounds like the kind of place you want to work, we’re looking forward to your application. It’s our policy to provide equal employment opportunity for all applicants and members of Riot Games, Inc. Riot Games makes reasonable accommodations for handicapped and disabled Rioters and does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, handicap, veteran status, marital status, criminal history, or any other category protected by applicable federal and state law. We consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with applicable federal, state and local law, including the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, and the Washington Fair Chance Act. Per the Los Angeles County Fair Chance Ordinance, the following core duties may create a basis for disqualifying candidates with relevant criminal histories: Safeguarding confidential and sensitive Company data Communication with others, including Rioters and third parties such as vendors, and/or players, including minors Accessing Company assets, secure digital systems, and networks Ensuring a safe interactive environment for players and other Rioters These duties are directly related to essential operations, safety, trust, and compliance obligations within our organization. Please note that job duties may evolve based on business needs and additional responsibilities may be assigned as necessary to maintain operational efficiency and security.

Location: Milan, Italy; Biassono, Italy Workplace: Hybrid Artificial Intelligence. Actual Impact. At Docebo, we’re using AI to change how people learn at work—and we mean actually change it. We’re an AI-powered learning platform that helps organizations create, deliver, and manage training all in one place. But our real mission goes deeper: we help teams move faster, work smarter, and focus on the work that truly matters. Our platform is built with intelligent, time-saving tools that personalize learning, eliminate busywork, and turn training from a checkbox into a superpower. The result? Better experiences for learners and real results for businesses. We’re shaping the future of learning with a team that isn’t afraid to challenge the status quo. If you're excited by the idea of using AI to make work-life better for real people–you’ll feel right at home here. And it’s not just what we build, it’s how we show up. At Docebo, our values aren’t just posters on the wall—they guide how we work every day. We call it the Docebo Heart: trust by default, assume positive intent, and create space for different perspectives to thrive. So… what are you waiting for? Join 900+ Docebians around the world and help us reinvent the way people learn, because learning never stops. 🚀 THE ADVENTURE AHEAD As our Data Engineer I, you will build and fine-tune the robust cloud data pipelines that power our analytics, data science, and product teams. You’ll tackle real-world architecture challenges, collaborate with world-class engineers, and directly shape how intelligent learning experiences reach millions of users worldwide. We are seeking engineers who bring deep analytical rigor to data modeling and relentless dedication to data quality.   🛠️ THE DAY-TO-DAY - Engineer the Flow: Implement high-performance ELT/ETL transformations using tools like dbt based on innovative specs from senior engineers and architects. - Orchestrate Data Jobs: Build, deploy, and maintain seamless data ingestion and transformation jobs powered by Airflow and Cloud Composer. - Guard Data Quality: Write and execute vital data tests—including row counts, null checks, and reference validations—to keep our datasets clean and trusted. - Monitor & Resolve: Proactively investigate automated alerts, ensuring peak system performance and escalating deeper architectural changes when needed. - Shape Lakehouse Architecture: Maintain pristine schemas and naming conventions across Snowflake, BigQuery, or Databricks while implementing table and view enhancements. - Document & Empower: Create clear documentation for pipelines, schemas, and caveats so the entire engineering team can safely build on your foundation. - Partner & Collaborate: Work closely alongside Analytics and Data Science teams to deeply understand their query needs and deliver optimized data structures. ⚡ YOUR SUPERPOWERS - Solid Academic & Technical Foundation: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience). - Early Career Momentum: 2-3 years of hands-on experience in data engineering, BI engineering, or equivalent project-driven environments. - SQL Mastery: Exceptional ability to write clean SQL (joins, filters, aggregations) and query across cloud data platforms like Snowflake, BigQuery, or Databricks. - ETL/ELT Aptitude: Strong foundational understanding of data transformation concepts and comfort navigating tools like dbt and Airflow under guided supervision. - Code & Version Control Literacy: Familiarity with Python (or similar languages) and Git-based collaborative workflows to ship clean code efficiently. OUR HYBRID WORK PHILOSOPHY 🤝 Great work can happen anywhere but coming together helps us go further. Our team spends three days a week in the office (Tuesday-Thursday) to collaborate, solve problems, and learn from each other. With flexibility the rest of the week, it’s a balance designed to help everyone do their best work and keep growing.   OUR TOTAL REWARDS PHILOSOPHY 🎉 Our Total Rewards Philosophy centers around three core areas to reward and care for our People: - Rewarding Impact: We lead with competitive pay to reward the impact, skills and traits that fuel our success. - Fostering Holistic Wellbeing: We care deeply about and invest in the whole person with programs that support our people’s physical, mental, and financial well-being. - Empowering Our Talent Culture: We build a culture of trust and empowerment by designing our rewards and benefits with transparency, equity, and flexibility, enabling our people to do their best work and stay for the long haul.   OUR PROMISE TO YOU 😍 - Financial Wellness: Own a piece of Docebo through our Employee Share Purchase Plan (ESPP) at a 15% discount, plus a competitive compensation package. - Your Well-Being, Covered: You’ll get access to health benefits, so you can get the care you need when you need it. - Rest, Relax, Repeat: Rest and recharge with paid vacation days, two company-wide Docebo Days, floating holidays for cultural celebrations, and your birthday off! - Family First: We provide coverage offering you time with your little one(s) so you can soak up all those precious moments. Fun fact: we had 30 Docebian babies join the family in 2025! - Fuel for Your Day: Receive a meal voucher for every working day, so you can focus on doing your best work while enjoying a meal that keeps you energized. - Connections That Count: Connect with global communities through our Employee Resource Groups (including PRIDE, DWA, BIDOC, and Green Ambassadors) and company-wide events that keep the fun rolling all year long.

Location: Toronto, Ontario Workplace: Hybrid Artificial Intelligence. Actual Impact. At Docebo, we’re using AI to change how people learn at work—and we mean actually change it. We’re an AI-powered learning platform that helps organizations create, deliver, and manage training all in one place. But our real mission goes deeper: we help teams move faster, work smarter, and focus on the work that truly matters. Our platform is built with intelligent, time-saving tools that personalize learning, eliminate busywork, and turn training from a checkbox into a superpower. The result? Better experiences for learners and real results for businesses. We’re shaping the future of learning with a team that isn’t afraid to challenge the status quo. If you're excited by the idea of using AI to make work-life better for real people–you’ll feel right at home here. And it’s not just what we build, it’s how we show up. At Docebo, our values aren’t just posters on the wall—they guide how we work every day. We call it the Docebo Heart: trust by default, assume positive intent, and create space for different perspectives to thrive. So… what are you waiting for? Join 900+ Docebians around the world and help us reinvent the way people learn, because learning never stops. 🚀 THE ADVENTURE AHEAD As our Data Engineer I, you will build and fine-tune the robust cloud data pipelines that power our analytics, data science, and product teams. You’ll tackle real-world architecture challenges, collaborate with world-class engineers, and directly shape how intelligent learning experiences reach millions of users worldwide. We are seeking engineers who bring deep analytical rigor to data modeling and relentless dedication to data quality. 🛠️ THE DAY-TO-DAY - Engineer the Flow: Implement high-performance ELT/ETL transformations using tools like dbt based on innovative specs from senior engineers and architects. - Orchestrate Data Jobs: Build, deploy, and maintain seamless data ingestion and transformation jobs powered by Airflow and Cloud Composer. - Guard Data Quality: Write and execute vital data tests—including row counts, null checks, and reference validations—to keep our datasets clean and trusted. - Monitor & Resolve: Proactively investigate automated alerts, ensuring peak system performance and escalating deeper architectural changes when needed. - Shape Lakehouse Architecture: Maintain pristine schemas and naming conventions across Snowflake, BigQuery, or Databricks while implementing table and view enhancements. - Document & Empower: Create clear documentation for pipelines, schemas, and caveats so the entire engineering team can safely build on your foundation. - Partner & Collaborate: Work closely alongside Analytics and Data Science teams to deeply understand their query needs and deliver optimized data structures. ⚡ YOUR SUPERPOWERS - Solid Academic & Technical Foundation: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience). - Early Career Momentum: 2-3 years of hands-on experience in data engineering, BI engineering, or equivalent project-driven environments. - SQL Mastery: Exceptional ability to write clean SQL (joins, filters, aggregations) and query across cloud data platforms like Snowflake, BigQuery, or Databricks. - ETL/ELT Aptitude: Strong foundational understanding of data transformation concepts and comfort navigating tools like dbt and Airflow under guided supervision. - Code & Version Control Literacy: Familiarity with Python (or similar languages) and Git-based collaborative workflows to ship clean code efficiently. OUR HYBRID WORK PHILOSOPHY 🤝 Great work can happen anywhere but coming together helps us go further. Our team spends three days a week in the office (Tuesday-Thursday) to collaborate, solve problems, and learn from each other. With flexibility the rest of the week, it’s a balance designed to help everyone do their best work and keep growing. OUR TOTAL REWARDS PHILOSOPHY 🎉 Our Total Rewards Philosophy centers around three core areas to reward and care for our People: - Rewarding Impact: We lead with competitive pay to reward the impact, skills and traits that fuel our success. - Fostering Holistic Wellbeing: We care deeply about and invest in the whole person with programs that support our people’s physical, mental, and financial well-being. - Empowering Our Talent Culture: We build a culture of trust and empowerment by designing our rewards and benefits with transparency, equity, and flexibility, enabling our people to do their best work and stay for the long haul. OUR PROMISE TO YOU 😍 - Financial Wellness: Own a piece of Docebo through our Employee Share Purchase Plan (ESPP) at a 15% discount, plus a competitive compensation package. - Your Well-Being, Covered: You’ll get access to health benefits, so you can get the care you need when you need it. - Rest, Relax, Repeat: Rest and recharge with paid vacation days, two company-wide Docebo Days, floating holidays for cultural celebrations, and your birthday off! - Family First: We provide coverage offering you time with your little one(s) so you can soak up all those precious moments. Fun fact: we had 30 Docebian babies join the family in 2025! - Connections That Count: Connect with global communities through our Employee Resource Groups (including PRIDE, DWA, BIDOC, and Green Ambassadors) and company-wide events that keep the fun rolling all year long. ABOUT DOCEBO 💙 At Docebo, we create seamless, AI-powered learning experiences for over 3,000 customers worldwide. We have successfully achieved two IPOs (TSX: DCBO & NASDAQ: DCBO https://www.docebo.inc/home/default.aspx), been recognized as a top SaaS e-learning solution, and are growing exponentially in the process. We're a global company, with office across North America, EMEA, APAC, and beyond. Our team is guided by five core values—Grow Together Win Together, Build with Our Customer, Clear is Kind, Own Outcomes, Progress Over Perfection—that shape everything we do. If this resonates with you, now is the perfect time to join one of the fastest-growing learning technology companies in the world. Docebo is an Equal Employment Opportunity employer https://www.eeoc.gov/sites/default/files/2022-10/EEOC_KnowYourRights_screen_reader_10_20.pdf. We are committed to diversity and inclusion in our workforce. All qualified applicants and employees will receive consideration for employment regardless of their race, colour, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, citizenship status, age, disability, genetic information, or any other category protected under applicable law. As a federal contractor, Docebo is committed to the principles of affirmative action and equal employment opportunity for protected veterans and individuals with disabilities. Docebo does not discriminate because of protected veteran status or on the basis of disability, and Docebo takes affirmative action to employ and advance in employment qualified protected veterans and individuals with disabilities. Any individuals requiring a reasonable accommodation or would like to voluntarily disclose a disability or protected veteran status to assist with their employment application should send an e-mail to their recruiter directly.

Компания Centicore ищет опытного инженера DevOps для работы над централизованной MLOps-инфраструктурой. Задачи: — Конфигурировать и разворачивать инфраструктурные компоненты — Адаптировать и разрабатывать с нуля автоматизированные пайплайны под актуальные бизнес-задачи (AutoML, LLM, computer vision и пр.) — Создавать и поддерживать пользовательские инструменты для работы в едином ML-контуре применения и обучения — Масштабировать разрабатываемые системы и инструменты для управления жизненным циклом моделей машинного обучения Требования: — Опыт DevOps от 5 лет с инструментами docker, helm, jenkins/gitlab CI, python — Желателен опыт ML/MLOps от 1 года с airflow, jupyterhub, mlflow, seldon, cuda — Опыт администрирования Kubernetes от 2 лет — Опыт работы с Hadoop, Spark, Kafka, ELK Условия: — Удаленный формат работы в IT аккредитованной компании — Оформление по ТК — Оплачиваемый отпуск 28 календарных дней в год и оплачиваемые больничные — Предоставление техники (MAC, Windows) — ДМС со стоматологией — Компенсация хобби/спортзала/английского языка — Компенсация профессионального обучения — Возможность участия во внутренних программах компании и активностях — Онлайн и оффлайн корпоративные мероприятия — Программа лояльности Сентибонусы

Задачи: — Разработка сервисов ИИ платформы для создания функциональных агентов и ИИ ассистентов. — Подготовка бэк части для функциональных агентов, включая среду разработки агентов: тестирование фреймворков, opensource агенты. — Разработка сервиса управления реестром и скиллами агентов. — Создание отдельного единого сервиса для всех баз знаний, интеграция крупных корпоративных источников. Требования: — Понимание архитектуры, возможностей и ограничений современных языковых моделей. — Опыт работы с embeddings, vector search, RAG, structured output и tool calling. — Понимание принципов проектирования AI-агентов: управление состоянием и контекстом, выбор инструментов, контроль уровня автономности и human-in-the-loop. — Опыт построения evals для LLM-систем, оценки качества ответов, контроля галлюцинаций и мониторинга поведения системы. — Понимание рисков prompt injection, некорректного tool calling, утечки данных и чрезмерных прав доступа агента. — Опыт работы с API языковых моделей, Hugging Face и одним из orchestration-фреймворков. — Умение оценивать стоимость, latency и масштабируемость LLM-решений. — Опыт работы в e-commerce, retail или других продуктовых highload-системах. — Опыт работы с Airflow, MLflow, Kafka, Redis, Kubernetes, Prometheus и Grafana. — Опыт эксплуатации GPU-инфраструктуры и оптимизации inference. — Опыт fine-tuning или адаптации LLM, включая LoRA / PEFT. — Опыт проектирования agentic workflows или multi-agent-систем. — Понимание принципов информационной безопасности, privacy и работы с персональными данными. Условия: — full-time

Группа компаний «ДОМ.РФ» реализует национальные проекты в области жилищного строительства и ипотеки. Более 25 лет занимается развитием жилищной сферы в России. В компании работает более 9 тысяч человек, из которых более 1,5 тысяч — команда цифровой вертикали. ИТ-команда проводит цифровую трансформацию строительной отрасли и создает инновационные ИТ-продукты для людей, бизнеса и государства. Описание: Ищем разработчика-аналитика DWH в направление Рисков. Позиция предполагает примерно 60-70% разработки, остальная часть — аналитика и взаимодействие с заказчиком. Задачи: — Проводить проектную коммуникацию с представителями бизнес-заказчика для уточнения бизнес-требований — Выявлять и анализировать бизнес-требования, потребности и проблемы заказчика — Формировать спецификации требований, описание логических моделей, структур данных и проектных решений (хранилища и витрины данных) — Согласовывать проектные решения с архитекторами, разработчиками и аналитиками смежных команд — Проектировать и разрабатывать ETL/ELT процессы, слои хранилища и витрины данных — Разрабатывать и оптимизировать SQL-запросы — Проводить внутреннее тестирование и контроль качества информационных продуктов — Участвовать в приёмочных испытаниях информационных продуктов и документации с заказчиками — Обеспечивать контроль качества данных и их целостность Требования: — Высшее образование: техническое, математическое или финансовое — Опыт работы не менее 5 лет — Уверенные знания Python, GIT, Airflow (DAG с декомпозицией задач, зависимостями, сенсорами) — Глубокое знание SQL: оконные функции, процедуры, функции, вложенные запросы, опыт оптимизации запросов — Понимание индексов, партиций, ключей, нормальных форм, SCD — Понимание моделирования данных, построения хранилища, репликации и консистентности данных — Умение рефакторить код и проводить миграцию из одной системы в другую — Знание принципов CI/CD Будет плюсом: — Знание PL/SQL для Oracle, Postres, Greenplum или иной MPP СУБД — Опыт работы с API — Знание ООП, DBT, Kafka, Bash, SAS, разработка кастомных Airflow операторов

Location: United Kingdom, London Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Role Overview: As a Senior / Staff Data Scientist, you will help shape how PlayStation measures the impact of products, features and commercial initiatives through world-class experimentation, causal inference and applied data science. Working at the intersection of data science, machine learning, product and commercial strategy, you will develop innovative measurement methodologies, define best practices, and provide technical leadership that enables teams across PlayStation to make faster, more confident business decisions. This role goes beyond traditional A/B testing. You will solve complex measurement problems where controlled experiments are difficult or impossible, applying modern causal inference, statistical modelling and machine learning techniques to generate robust business insights. You will collaborate closely with product, engineering, analytics and business teams to raise the standard of experimentation and evidence-based decision-making across the organisation. This is an opportunity to influence strategic decisions impacting millions of PlayStation players while helping establish best-in-class measurement practices across one of the world's leading entertainment companies. What You'll Be Doing: Develop innovative measurement methodologies across experimentation, causal inference and advanced analytics that improve the quality, reliability and scalability of business decision-making across PlayStation. Design robust measurement approaches across randomised experiments, quasi-experimental methods and observational causal inference where controlled experimentation is impractical. Research, evaluate and prototype statistical, machine learning and AI techniques to solve complex real-world measurement challenges. Apply advanced data science and machine learning techniques where appropriate to complement experimentation and support complex business decisions. Define and promote best practices for experiment design, statistical analysis and causal measurement across multiple product and business teams, helping scale experimentation across PlayStation. Partner closely with senior business leaders, product managers, engineers and analysts to identify high-impact measurement opportunities and ensure product and commercial decisions are supported by rigorous evidence. Partner with engineering teams to influence the evolution of PlayStation's experimentation platform and measurement capabilities, ensuring statistical best practices are embedded into future platform development. Provide technical leadership across multiple product and commercial domains, influencing measurement strategy, analytical standards and long-term technical direction through expertise and collaboration. Mentor and develop analysts and data scientists, helping raise technical capability and analytical excellence across the team. Champion a culture of evidence-based decision-making by educating stakeholders, sharing best practices, and communicating advances in experimentation, causal inference and applied data science. Stay current with emerging developments in statistics, machine learning and AI, identifying opportunities to improve measurement capabilities and analytical workflows. Represent the team across PlayStation, building strong partnerships, engaging senior stakeholders, and promoting technical excellence in data science and experimentation. What We're Looking For: Required: Master's degree (or equivalent industry experience) in Statistics, Economics, Mathematics, Computer Science, Data Science or another quantitative discipline. PhD preferred. Strong industry experience (5+ years) applying experimentation, causal inference, statistical modelling and measurement science to solve complex business problems. Deep expertise in experimental design, A/B testing and modern causal inference methods, including quasi-experimental approaches. Experience applying advanced data science, machine learning and statistical modelling techniques to solve complex business problems. Expert-level proficiency in Python and SQL for large-scale analytical workflows. Experience developing reusable analytical methodologies, frameworks or libraries that improve measurement quality and consistency across teams. Demonstrated ability to influence technical direction, promote best practices and drive adoption through collaboration, technical leadership and senior stakeholder engagement. Strong stakeholder management, executive communication and influencing skills, with the ability to translate complex analytical concepts into clear business recommendations for both technical and non-technical audiences. Curiosity, creativity and a pragmatic approach to solving ambiguous, high-impact business problems. Preferred: Experience working in gaming, digital products, consumer technology, e-commerce or subscription businesses. Familiarity with modern data engineering and analytics tooling such as Databricks, Snowflake, Git and Airflow. Experience with modern business intelligence and data visualisation tools such as Tableau, Domo or Power BI. Experience applying AI tools, including large language models (LLMs), to improve data science workflows and analytical productivity. Demonstrated technical leadership through mentoring, publications, conference presentations, open-source contributions or internal communities of practice. What You'll Influence: Measurement strategy for PlayStation's largest product and commercial initiatives. Experimentation best practices adopted across multiple business functions. Strategic product and commercial decisions through rigorous causal evidence. The evolution of PlayStation's measurement and experimentation capabilities. Technical excellence in experimentation, causal inference and applied data science across the organisation. Why You'll Love This Role: Shape how PlayStation measures product and commercial success across a global business. Influence strategic decisions affecting millions of players worldwide. Solve challenging real-world problems using cutting-edge experimentation, causal inference and machine learning techniques. Collaborate with talented scientists, engineers and product leaders on high-impact initiatives. Drive the adoption of best practices in experimentation and evidence-based decision-making across PlayStation. Continue developing your technical expertise while mentoring and growing others. Benefits: Discretionary bonus opportunity Hybrid Working (FlexModes) Private Medical Insurance Dental Scheme 25 days annual leave On-site gym Subsidised café Complimentary soft drinks On-site bar Secure cycle storage and shower facilities Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Вакансия предполагает работу на позиции Senior Data Science специалиста с полной занятостью и удалённым форматом в России. Основные задачи включают разработку сервисов для ИИ платформы, предназначенной для создания функциональных агентов и ИИ ассистентов, а также подготовку серверной части для таких агентов, включая тестирование фреймворков и работу с opensource агентами. В обязанности также входит создание сервиса управления реестром и навыками агентов, а также разработка единого сервиса для баз знаний с интеграцией крупных корпоративных источников. Кандидат должен хорошо разбираться в архитектуре и ограничениях современных языковых моделей, иметь опыт работы с embeddings, vector search, RAG, structured output и tool calling. Важны знания принципов проектирования AI-агентов, включая управление состоянием и контекстом, выбор инструментов, контроль автономности и human-in-the-loop. Требуется опыт построения evals для LLM-систем, оценки качества ответов, контроля галлюцинаций и мониторинга поведения, а также понимание рисков, связанных с prompt injection, некорректным tool calling, утечкой данных и избыточными правами доступа. Необходим опыт работы с API языковых моделей, Hugging Face и одним из orchestration-фреймворков, умение оценивать стоимость, задержки и масштабируемость LLM-решений. Желателен опыт в e-commerce, retail или других высоконагруженных продуктовых системах, а также владение инструментами Airflow, MLflow, Kafka, Redis, Kubernetes, Prometheus и Grafana. Кандидат должен иметь опыт эксплуатации GPU-инфраструктуры и оптимизации inference, а также fine-tuning или адаптации LLM, включая LoRA/PEFT. Опыт проектирования agentic workflows или multi-agent систем и понимание принципов информационной безопасности, конфиденциальности и работы с персональными данными также обязательны.

Задачи: — Проводить поиск, разбор и последующий анализ источников данных, предоставляющих информацию по уязвимостям; — Заниматься разработкой и формированием экспертизы (технические стандарты безопасности и информация по уязвимостям) в области информационной безопасности для продуктов компании; — Автоматизировать процессы по формированию экспертизы ИБ; — Взаимодействовать с продуктовыми командами с целью улучшения работы продуктов. Требования: — Знание Git, Python; — Наличие навыков администрирования Windows, Linux систем; — Опыт проведения работ по инструментальному анализу защищенности или опыт работы с одним из сканеров безопасности (Nessus, Nexpose, Qualys, MaxPatrol, OpenVAS, RedCheck, nmap); — Опыт администрирования сетевого и иного оборудования; — Знание актуальных угроз и уязвимостей на Windows/ Linux платформах; — Опыт применения методологий по описанию, приоритизации и устранению уязвимостей (CVE, CVSS, VPR, OWASP); — Понимание принципов работы инструментов автоматизации (Apache Airflow).

Проект Крупный ритейл мебели и товаров для сна. Ожидания от кандидата - Понимание теории реляционных баз данных и архитектуры хранилищ данных, включая ключевые структуры и процессы; - Владение методологиями проектирования и построения хранилищ данных; - Знание подходов к формированию управленческой отчётности: умение готовить данные для витрин, выполнять выверки и контролировать целостность информации; - Практическое применение Python в разработке решений для обработки и трансформации данных; - Опыт работы с Airflow и понимание принципов оркестрации ETL‑процессов; - Уверенное владение SQL, в том числе при работе с большими объёмами данных; - Опыт использования DBT для построения и поддержки витрин данных; - Практический опыт работы с ArenadataDB, Postgres и Greenplum; понимание особенностей обработки данных в MPP‑системах; - Навыки оптимизации запросов для повышения производительности и снижения нагрузки на хранилище; - Умение работать в команде по принципам командной разработки, владение Git для контроля версий и совместной работы с кодом. Задачи на проекте - Оптимизация и выверка запросов для построения витрин отчетности. Условия работы ТК РФ до 150 к на руки ИП РФ до 1100 руб/час

Location: New York, NY (HQ) Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. ABOUT THE ROLE The Applied Science team builds models and tools that solve Ramp’s most critical problems: from underwriting businesses to combatting fraud to making spend management smarter. We’re deeply embedded in the business and provide a quantitative foundation for decision making. As an Applied Science intern, you’ll be a fully integrated member of the team and own your project from start to finish. Working with engineers, product managers, and business stakeholders, you’ll translate complex business needs into scalable machine-learning-driven solutions. This is a chance to apply ML concretely, ship code, and create genuine value for Ramp and our customers. You will focus on exciting problems in areas like: credit, fraud, growth, or our core product.   WHAT YOU’LL DO - End-to-End ML: own the model lifecycle from data exploration and feature engineering to training, benchmarking, deployment, and monitoring - State-of-the-Art AI: leverage the latest Large Language Models (LLMs) to solve novel problems and create new product capabilities for our customers - Versatile Techniques: apply the right tools to the right problems, whether it’s deep learning, gradient boosting, or causal inference - Rigorous Experimentation: quantify the impact of your work through A/B tests and other statistical methods - Collaborate: partner closely with product and business leaders to translate models and insights into actionable strategy and user-facing features   WHAT YOU NEED - B.S., M.S. or Ph.D. Student: currently pursuing a degree in Data Science, Computer Science, Math, Physics, Economics, Statistics, or other quantitative fields with an expected graduation date between Dec 2027 - 2029. Graduate degrees are preferred, but not a must. - Strong ML Fundamentals: solid understanding of the mathematical foundations of machine learning, statistics, probability, and optimization - Strong Interest or Experience with AI: curiosity and drive to integrate cutting edge LLMs and agents into applied solutions - Python Proficiency: good grasp of common Data Science libraries (pandas, scikit-learn, NumPy, PyTorch, etc.) - SQL Knowledge: experience wrangling data in a modern data warehouse (e.g. Snowflake, BigQuery, Redshift, Clickhouse) - Practical Experience: track record of curating datasets and building/evaluating ML models - Strong Communication: ability to clearly explain complex concepts to both technical and non-technical audiences and use data to build a compelling narrative - Bias For Action: a comfort with ambiguity and desire to ship solutions quickly then iterate   NICE TO HAVES - Publications, Projects, or Previous Experience: relevant experience applying AI/ML and demonstrating your passion for the field - Production ML Mindset: knowledge of software engineering best practices applied to ML including version control (Git), testing, and writing maintainable code - Data Orchestration: experience with leveraging modern data orchestration platforms (Airflow, Dagster, Prefect, Metaflow)   COMPENSATION - The monthly rate for this internship is $12,500 USD + housing stipend   RAMP BENEFITS - Apple MacBook - Catered lunches in NYC office Monday-Friday - Weekly coffee stipend BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

Задачи: — Проектирование, развёртывание и развитие инфраструктуры платформы искусственного интеллекта (on-prem и облако): Kubernetes-кластеры, GPU-ноды, storage, сети, безопасность. — Построение и поддержка CI/CD/CT-пайплайнов для ML-моделей и сервисов: от артефакта DS до продакшн-инференса с откатами и канареечными релизами. — Интеграция и эксплуатация LLM/ML-сервисов для внутренних команд и рыночных внедрений: упаковка моделей в сервисы, конфигурация autoscaling, SLA, observability. — Настройка мониторинга и алертинга по инфраструктуре и моделям (метрики ресурсов, latency, ошибки, дрейф данных), реагирование на инциденты, участие в post-mortem. — Автоматизация ML-пайплайнов: подготовка данных, обучение, переобучение, регистрация моделей, управление версиями и окружениями. — Поддержка внедрений у клиентов: тиражирование платформенных компонентов, адаптация инфраструктуры под контур заказчика, участие в performance-оптимизации и cost-cutting (GPU/CPU). Требования: — Высшее образование. — Опыт работы: 5+ лет. — Глубокое понимание жизненного цикла ML-модели: от экспериментов до продакшн-инференса и мониторинга. — Уверенный Linux, сетевые основы, безопасность, работа с контейнерами и оркестраторами. — Опыт построения CI/CD-пайплайнов для сервисов и ML-артефактов (Docker images, модели, данные). — Опыт с LLM/GenAI-платформами: vLLM, TGI, Hugging Face, Triton/ONNX Runtime, оптимизация инференса. — Опыт построения feature store, model registry, ML pipelines (Kubeflow, MLflow, Airflow, Argo Workflows). — Работа в гибридной инфраструктуре: on-prem кластеры + публичные облака (T1 Cloud, VK Cloud, Yandex Cloud и др.). — Понимание cost-optimization: FinOps для GPU/CPU, автобалансировка нагрузок. — Языки: Python (утилиты, обвязка) / Bash. — Контейнеризация и оркестрация: Docker, Docker Compose, Kubernetes (Helm, Operators), желательно Kubeflow. — CI/CD: GitLab CI / GitHub Actions / Jenkins / Argo CD. — Инфраструктура: Terraform / Ansible, конфигурация Linux-серверов, Nginx, cert-manager. — Data/ML-инфраструктура: Apache Kafka, Airflow/Argo Workflows, S3-совместимые хранилища, MLflow/Weights&Biases. — Мониторинг и логи: Prometheus, Grafana, Loki/ELK, Sentry, alertmanager. Профессиональные навыки: — Проектирование и развёртывание инфраструктуры платформы ИИ (кластер Kubernetes, storage, сети, security) под LLM/ML-нагрузки. — Построение воспроизводимых ML-пайплайнов: обучение, валидация, packaging модели, деплой в batch и real-time режимах. — Настройка мониторинга моделей: метрики качества, дрейф данных, latency/throughput, автоматический rollback и retraining-триггеры. — Совместная работа с DS/разработчиками и внедренцами: перевод экспериментальных ноутбуков в надёжные сервисы для внутренних и внешних клиентов. Специализированные программы: — GitLab / GitHub, Jira / Confluence/Сфера — Kubernetes ecosystem: kubectl, Helm, K9s, Lens. — ML-инструменты: MLflow / Weights & Biases, Kubeflow Pipelines / Airflow, model registry. — Мониторинг: Prometheus, Grafana, Loki/ELK, Alertmanager, Sentry.

Проект: Разработка единой системы оценки эффективности. Задачи: — Разработка решений вокруг DWH — Создание ETL / настройка сбора данных, BI-аналитика — Проработка архитектуры данных и системы для их сбора — Внедрение нового функционала, сокращение техдолга Требования: — Опыт проектирования и построения пайплайнов: Python / Apache Airflow — Интеграция источников с GraphQL, RESTful — Хранение данных в Apache Iceberg / проектирование архитектуры хранения данных — Проектирование архитектуры сбора данных и витрин — Тестирование данных на всех уровнях требований к качеству данных — Настройка метрик и алертов Prometheus / интеграция с внутренними системами оповещений — Знание предметной области Условия: Удаленный формат работы Интересный проект с архитектурными задачами Профессиональный рост

О нас: Мы строим ведущую платформу классифайд объявлений Узбекистана. ML - ядро нашего продукта: поиск, ранжирование, модерация и рекомендации. Команда находится на активной стадии построения ML-инфраструктуры с нуля: мы выбираем технологии, формируем процессы и закладываем фундамент для ML-платформы следующего поколения. Чем предстоит заниматься: - Развивать качество поиска: LTR-модели (LambdaMART/LightGBM), разрабатывать и интегрировать векторный поиск, улучшение поискового пайплайна (работа с саджестером, catpred). - Строить ML-модели для Trust & Safety: обнаружение мошенничества, модерация объявлений, NER в мессенджерах, скоринг нарушений. - Развивать систему рекомендаций - item2item, user2item рекомендации. - Участвовать в проектировании и развёртывании ML-платформы (SageMaker, MLflow, Feast, Evidently, Airflow).