Вакансии

Задачи: — Подключать новые источники данных — Разрабатывать и модернизировать существующие решения

Location: Тамбов Приглашаем на работу специалистов для создания продуктов с использованием искусственного интеллекта. Обязанности * осуществляет обработку текстовых, голосовых, графических/видео файлов для использования в машинном обучении моделей в соответствии с установленными правилами и методологией. * производит транскрибацию аудиофайлов в соответствии с установленными правилами и по разработанной методологии. Требования * образование не ниже среднего профессионального (хорошее знание русского языка) * знание программ: Excel, Word * готовность к монотонной, потоковой технологичной работе. Условия * стабильный оклад и социальная поддержка сотрудников * график работы: (5-дневная рабочая неделя, выходные - суббота, воскресенье) * удалённая работа на территории г. Тамбов * официальное оформление с первого дня * расширенный ДМС и льготное страхование для семьи * подписка Прайм с возможностью совместного использования на трёх близких * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ

Location: Москва Наша команда отвечает за качество моделей TTS в GigaChat – той части нейросети, которая занимается звуком. Сейчас мы ищем ребят, которые будут работать над базовым качеством, мультимодальным Гигачатом и другими классными проектами. _Первый этап отбора на эту вакансию – общение с AI-рекрутером. После отклика вам на почту и в чат на платформе HeadHunter придет приглашение пройти первичное интервью с ГигаРекрутером в Telegram. Диалог займёт примерно 10 минут. Его задача — уточнить недостающие детали и ускорить рассмотрение вашей кандидатуры. ГигаРекрутер только начинает свой путь, поэтому просим относиться с пониманием. Ваш опыт и участие помогут сделать его удобным и полезным!_ Обязанности * Писать пайплайны обработки аудио/видео данных. * Хотим обрабатывать десятки тысяч часов в день на кластере * инферить опенсорсные и внутренние модели на гпу (денойзер, распознавание, тулы для оценки качества аудио) * поддержка сетевого хранилища данных Требования * python и алгоритмы на хорошем уровне * опыт с multithreading и multiprocess * опыт обучения и/или инференса тяжелых моделей на нескольких гпу * s3, dvc, git Условия * гибридный или удаленный формат работы * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров

Location: Москва Сбер в поиске Аналитика данных в Управление обеспечения устойчивого функционирования Обязанности * формирование регулярной аналитической и статистической отчетности * разработка новых форм отчётности, ее автоматизация и визуализация * подготовка информационных и презентационных материалов по итогам проведенных аналитических работ по различным тематикам * предоставление оперативной аналитики для внутренних заказчиков * анализ причин, повлиявших на возникновение нештатный ситуаций и подготовка рекомендаций по предупреждению их возникновения, определение новых видов угроз * подготовка прогноза возможных нештатных ситуаций, на основе имеющейся базы, открытых источников информации, а также анализа выявленных проблем и рисков * участие во внедрении комплексной автоматизации в производственные процессы * подготовка технических заданий на создание средств автоматизации требования. Требования * высшее образование (математическое, экономическое, техническое) * опыт аналитической работы от 3-х лет * практический опыт подготовки аналитических отчётов и презентаций (портфолио) для руководителей * умение работать с большими массивами данных * готовность к гибкому графику работы в случае выполнения срочных поручений руководства * личностные характеристики: стрессоустойчивость, дисциплинированность, коммуникабельность, умение работать в режиме многозадачности, ориентированность на результат * структурное и системное мышление, сильные аналитические навыки * знание офисных программ excel, power point на уровне продвинутого пользователя (знание Power BI, Python будет конкурентным преимуществом) * будет преимуществом: навыки работы с ИИ. Условия * официальное трудоустройство * работа в крупнейшем банке России * формат дохода: оклад + годовая премия * комфортный офис по адресу: пр-кт Кутузовский, 32 к3 стрБ * уникальная система обучения Сбера для профессионального и карьерного развития * расширенный ДМС и льготное страхование семьи * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких.

Задачи: — Разработка предиктивных моделей для application-скоринга и смежных областей по внутренним запросам бизнеса, включая подбор алгоритмов, тестирование гипотез, оценку качества моделей и внедрение решений в продуктовые процессы. — Развитие внутренней базы знаний, стандартизация фреймворков, поддержка feature store, формирование системы отчётности по моделям и метрикам для прозрачности и воспроизводимости результатов. — Адаптация результатов прогнозирования для применения в кредитной политике, перевод технических выводов на язык бизнес-показателей, предложение вариантов интеграции моделей в лимитирование, сегментацию и правила принятия решений. — Валидирование сторонних сервисов и данных, проведение технических и бизнес-тестов интеграций, оценка качества внешних источников, согласование условий и SLA с вендорами. — Выполнение аналитических задач по запросу подразделений: исследование поведения клиентов, оценка влияния продуктовых изменений, поиск точек роста и узких мест в воронке, подготовка обоснований для управленческих решений. — Менторство и обучение младших сотрудников, помощь в освоении инструментов и методологий, разбор ошибок, развитие навыков работы с данными и построения моделей, участие в формировании культуры качества в команде. — Контроль сроков и качества выполнения задач, постановка чётких целей, отслеживание прогресса, управление приоритетами, обеспечение соответствия результатов внутренним стандартам и регуляторным требованиям. Требования: — Высшее образование в области IT, математики или менеджмента организации. — Опыт работы в финансовой сфере не менее 5 лет. — Уровень английского языка от B1, достаточный для чтения технической документации и коммуникации с вендорами. — Опыт управления или работы в команде от 3 человек; опыт наставничества и передачи знаний будет преимуществом. — Обязательный опыт работы с кредитными продуктами, понимание жизненного цикла кредита, механик скоринга, лимитирования и управления портфелем. — Практический опыт разработки рисковых моделей от постановки задачи до валидации и внедрения, включая работу с несбалансированными данными, выбор метрик и интерпретацию результатов. Условия: — Профессиональное развитие с участием в разных проектах холдинга, созданием новых направлений, влиянием на бизнес-процессы и свободой в принятии решений. — Прокачка скиллов через решение нестандартных задач, карт-бланш на смелые идеи и возможность быстро видеть реальный результат. — Карьерный рост с пониманием необходимых шагов для перехода на другой уровень. — Работа в динамичной среде с новыми вызовами каждый день. — Комфортный офис в пешей доступности от м. Шелепиха. — Возможность гибридного графика работы в Москве. — Высокий уровень дохода (оклад + премии), размер заработной платы обсуждается по итогам собеседования. — Привлекательный компенсационный пакет: программа ДМС с стоматологией и обслуживанием в лучших клиниках города, 8 дополнительных дней к отпуску, корпоративное такси, обучение и участие в конференциях, курсы английского или испанского, скидка на сервис заботы о благополучии и личностном росте, корпоративная система скидок BestBenefits.

Задачи: — Разработка end-to-end data-сервисов на базе ML-моделей для снижения стоимости риска, включая онлайн-модели оценки вероятности дефолта новых клиентов, антифрод-модели и модели для коллекшн. — Построение и валидация скоринговых моделей для микрофинансового бизнеса с применением feature engineering, выбором алгоритмов, оценкой качества и переобучением. — Регулярный анализ новых источников данных и расчет экономической эффективности их подключения для снижения стоимости риска. — Управление полным жизненным циклом моделей: от постановки бизнес-задачи до мониторинга качества и переобучения. — Расчет финансового эффекта от моделей и подготовка презентаций для бизнес-пользователей. Требования: — Опыт промышленной разработки на Python. — Обязательный опыт разработки и внедрения скоринговых, коллекшн и антифрод-моделей в кредитной или микрофинансовой сфере. — Понимание жизненного цикла ML-моделей, включая мониторинг и переобучение. — Навыки валидации моделей, знание ключевых метрик и аналитики. — Опыт feature engineering. — Знание классических алгоритмов машинного обучения. — Опыт работы с одним из фреймворков Django, Flask или FastAPI. — Хорошее владение SQL и опыт работы с Git. — Знание математической статистики. — Профильное образование, предпочтительно сильный математик или чистый Data Scientist. Условия: — Удалённый формат работы или гибридный режим в Москве. — ДМС, возможность выбора техники, профессиональное обучение. — Работа в аккредитованной ИТ-компании. — Свобода в использовании сложных алгоритмов и подходов при их целесообразности. — Полный цикл моделирования: от feature engineering до внедрения и мониторинга. — Быстрое влияние на бизнес-результаты без бюрократии.

Задачи: — Работа с реляционными базами данных PostgreSQL и MS SQL; — Участие в разработке ETL-процессов; — Работа с аналитической базой данных ClickHouse; — Визуализация данных, создание дашбордов, презентаций и табличных отчетов, а также табличных инструментов для моделирования различных сценариев; — Анализ и очистка данных; — Решение инцидентов с качеством данных; — Обучение коллег и руководителей работе с данными, отчетами и дашбордами. Требования: — Продвинутый уровень владения T-SQL (MS SQL), ETL-инструментами на базе MSSQL / Apache Airflow, PostgreSQL, ClickHouse; — Опыт разработки на Python; — Опыт работы с инструментами визуализации данных Power BI; — Опыт проектирования аналитических систем хранения и обработки больших данных не менее 3 лет; — Базовые навыки работы с Git; — Высшее техническое образование. Условия: — Оформление согласно трудового законодательства РФ; — Официальное трудоустройство по срочному трудовому договору на 1 год с возможностью пролонгации (в соответствии с ТК РФ); — Своевременная и полностью официальная заработная плата; — ДМС через 3 месяца после трудоустройства; — Возможность профессионального и карьерного развития; — Рабочее место в офисе компании рядом с м. Маяковская (пешая доступность); — График работы: понедельник-пятница с 9:00 до 18:00.

Location: Ukraine; Poland M Science is a data-driven research and analytics firm, uncovering new insights for leading financial institutions and corporations. We come to work every day to create actionable insights from alternative data. M Science is revolutionizing research, discovering new data sets and pioneering methodologies to provide actionable intelligence. Our research teams have decades of experience working with massive amounts of unstructured data in near real-time to discern critical insights that help clients make smarter, more informed decisions. We combine the best of finance, data and technology to create a truly unique value proposition for both financial services firms and major corporations. We're looking for a Senior Data Engineer who is passionate about building robust, scalable data infrastructure and pipelines. You'll work with petabytes of data daily using cutting-edge technologies including Databricks, SQL, Python, Airflow, and modern AI development tools. This role requires someone who can architect solutions, mentor junior engineers, and drive technical excellence across our data platform. NOTE: This role requires flexible working hours to ensure at least 2 hours of overlap with colleagues based on both the US East Coast and West Coast. Expected working schedule: 12:00–20:00 CET. Responsibilities: Design, build, and maintain scalable ETL/ELT pipelines processing petabytes of data; Architect data solutions using modern cloud technologies (AWS, Databricks, Snowflake); Implement real-time and batch processing workflows using Apache Airflow; Optimize data storage and retrieval patterns for analytical workloads; Implement comprehensive data quality frameworks and monitoring systems; Establish data governance standards and best practices; Build automated testing and validation for data pipelines; Develop data lineage and metadata management solutions; Lead technical design reviews and architecture decisions; Mentor junior data engineers and promote best practices; Drive adoption of modern development practices including CI/CD for data pipelines; Leverage AI coding assistants (Cursor, Claude, GitHub Copilot) to accelerate development; Implement AI-driven data quality checks and anomaly detection; Stay current with emerging AI tools and their applications in data engineering. Requirements: 3-8 years of data engineering experience; Expert-level SQL - Complex queries, performance optimization, window functions; Advanced Python - Object-oriented programming, async/await, data processing libraries; Apache Airflow - DAG development, custom operators, workflow orchestration; Databricks - Spark optimization, Delta Lake, Unity Catalog governance; Cloud Platforms - AWS (S3, EMR, Lambda, Glue) or equivalent; Data Modeling - Dimensional modeling, data vault, modern data stack patterns; Experience mentoring junior engineers and leading technical initiatives; Strong communication skills for cross-functional collaboration; Experience with Agile methodologies and project management; Ability to translate business requirements into technical solutions. Will be a plus: Experience in financial services, fintech, or data intelligence companies; Background in consumer retail, digital media, or technology verticals; Advanced degree in Computer Science, Statistics, Mathematics, or related field. What we offer: 20 days of vacation; Public holidays; Mobilization reservation available upon successful completion of the probation period (for Ukrainian colleagues); The exchange of experience and work with talented colleagues; Last but not least - valuable compensation for your efforts.

Location: Ukraine; Poland M Science is a data-driven research and analytics firm, uncovering new insights for leading financial institutions and corporations. We come to work every day to create actionable insights from alternative data. M Science is revolutionizing research, discovering new data sets and pioneering methodologies to provide actionable intelligence. Our research teams have decades of experience working with massive amounts of unstructured data in near real-time to discern critical insights that help clients make smarter, more informed decisions. We combine the best of finance, data and technology to create a truly unique value proposition for both financial services firms and major corporations. We’re looking for exceptional data engineers who will work with petabytes of data daily using diverse technologies like Databricks, SQL, Python, Airflow, and others. You will be expected to effectively partner with upstream engineering teams and downstream consumers, including analysts and product engineers. In this role, you will build datasets to support analytics, experimentation, and machine learning. Specifically, you will build out standards for pipelining at scale, including establishing common operators, workflows/DAGs, and optimizing existing products. NOTE: This role requires flexible working hours to ensure at least 2 hours of overlap with colleagues based on both the US East Coast and West Coast. Expected working schedule: 12:00–20:00 CET. Responsibilities: Manage and improve pipeline for cleansing, filtering, and structuring raw datasets for the purposes of company and customer analysis. Implement QA procedures to proactively identify data anomalies and issues. Implement new products and features created by analyst teams using best practices. Work with other team members and internal stakeholders on infrastructure projects, documentation, QA and code reviews. Build and manage Repos. Continuously seek opportunities to standardize code and automate production pipelines. Create and contribute to frameworks that improve the efficacy of logging data, while working with infrastructure to triage issues and resolve. Mentor team members by giving/receiving actionable feedback. Requirements: 3-7 years of experience as a data engineer. Preference given to those that have worked in a similar type role within a data intelligence company or within our desired verticals including consumer retail & brands, digital media and technology. Expertise in data engineering in a cloud environment (Python, Databricks, AWS, Airflow, Snowflake, SQL). SQL & Python required. Working knowledge of PySpark and distributed computing. Strong people and management skills to interact with product managers and data analysts. Familiarity with software development methods such as Scrum and TDD. General project management skills. Ability to clearly articulate project plans, technical requirements, methodologies, and takeaways to both technical and non-technical stakeholders. Bachelor’s or higher degree, or significant experience in Statistics, Mathematics, Computer Science, or a similar quantitative discipline. Ability to thrive in a culture of quality and personal accountability. What we offer: 20 days of vacation. Public holidays. Mobilization reservation available upon successful completion of the probation period (for Ukrainian colleagues). The exchange of experience and work with talented colleagues. Last but not least - valuable compensation for your efforts.

анализ данных существующих автоматизированных систем. разработка прототипов SQL и подготовка эталонных данных.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As part of our growing Data Science and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You bring experience from high-bar teams and are eager to help shape the norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world. You’ll be the senior data science leader embedded in our Growth team and a strategic thought partner to its product and marketing leads. The Growth team’s job is to keep Anthropic on the exponential by getting more people and companies using and paying for Claude. You’ll help set direction across acquisition, activation, retention, and monetization: bringing a point of view on where to invest, how to grow revenue, and how to deliver more value for our users. As a tech lead manager, you’ll also manage a small team of Product Data Scientists, setting their agenda and raising the bar for how data shapes growth decisions. Key responsibilities Act as a strategic thought partner to Growth product and marketing leads, helping shape growth strategy, roadmap priorities, and the major bets the team makes Set the data science agenda for Growth, deciding where the Product Data Science team can have the most impact and what it should deprioritize Manage and develop a small team of Product Data Scientists Frame the most important and ambiguous questions facing Growth, from where the next users and companies will come from to what makes them stay and pay, size the opportunities, and make clear recommendations, including when they challenge the current direction Define how Growth measures success, establishing the core metrics, guardrails, and measurement frameworks that tie short-term wins to long-term value Set the standard for experimentation and causal inference across Growth, including holdouts and quasi-experimental methods where A/B tests aren’t practical, and personally lead the highest-stakes analyses Guide how Growth measures the return on its programs, including marketing incrementality, LTV, CAC, and payback, and turn the results into budget and channel recommendations Guide the team’s work on targeting and personalization, including how we decide which users get which message, offer, or experience and how we evaluate those decisions Turn complex analyses into clear narratives and recommendations that drive decisions with senior leadership and cross-functional partners Establish foundational data practices and shape the analytics infrastructure Growth needs to support rapid iteration and decision-making as our products grow Minimum qualifications Have extensive experience in data science, including leading analytics for a growth or product area at significant scale Have 5+ years managing data science teams, including setting team direction and developing senior data scientists Have been a trusted partner to senior product, growth, or marketing leaders, with clear examples of changing a roadmap or high-stakes decision Bring strong product sense and business judgment across the growth funnel, including the ability to frame the right question and take a position with incomplete information Have deep expertise in experimental design, causal inference, and statistical modeling at high scale, with enough fluency in Python and SQL to stay hands-on and guide others’ technical work Communicate clearly in writing and in presentations, including to executive audiences Have a bias for action and thrive in ambiguous, fast-moving environments where you must create clarity and drive progress Care about the company’s mission of building helpful, honest, and harmless AI Preferred qualifications Experience measuring marketing incrementality, including holdouts, geo experiments, or marketing mix modeling Experience with targeting, personalization, or uplift models in production Background at consumption-based, multi-product companies serving both consumers and enterprises Experience with AI/ML products or large language models, including using coding agents in your own analytical workflow The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $460,000 — $460,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco, CA • New York, NY • United States Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! Figma is building the system of record for digital product development: collaborative tools with natural interfaces that bring PMs, developers, QA, and designers together. That creates new-to-the-world problems in how we build AI products, understand how people use them, and evaluate what makes them useful. You'll work with a unique, petabyte-scale dataset with rich traces of collaborative design and product building to tackle those problems. We're looking for an Applied Data Researcher who combines scientific depth with a proactive builder mindset. Reporting directly to our Head of Data, with regular visibility with C-level executives, you'll identify and vet opportunities, lead investigations into critical questions, and build useful Data products, tools, and prototypes. You'll work across Data Science, Engineering, Product, Design, and AI Research, building directly and providing technical direction to teams of other scientists as efforts grow. This is a full time role that can be held from one of our US hubs or remotely in the United States. What you'll do at Figma: Identify and vet consequential opportunities across Data products and tools, AI measurement and evaluation, and applied data capabilities; lead investigations and shape priorities with partners Develop and validate methods for evaluating generative AI and agentic workflows, including representative datasets, baselines, rubrics, and evals; investigate uncertainty, bias, failure modes, and real user outcomes Personally write code and build or adapt LLM-powered tools, agentic workflows, models, prototypes, and demos that test scientific, product, and tool hypotheses, using AI-assisted coding and analysis in your work Provide technical direction and oversight for larger projects, delegate work to teams of senior and staff scientists, and mentor scientists and engineers while maintaining a substantial personal contribution to code and methods Independently drive delivery or partner with engineering to turn useful prototypes into maintained systems, balancing quality, latency, cost, reliability, and iteration speed Establish reusable methods, tools, and technical standards; iterate with users, drive adoption across teams, and use evidence to improve an approach or recommend a change in direction Communicate findings, recommendations, limitations, and tradeoffs clearly to partners and C-level executives, connecting scientific and technical work to important product and company decisions We'd love to hear from you if you have: Deep expertise in machine learning, statistics, causal inference, or a related quantitative field, plus hands-on experience in generative AI evaluation and measurement, including evals, labeling, and agentic workflows Fluent programming skills in Python or a comparable language, plus SQL, and a record of independently building, debugging, testing, and reviewing useful software, including working effectively with AI-assisted development tools A proactive builder mindset and end-to-end ownership: experience identifying important opportunities and carrying ambiguous work through method development, implementation, rigorous evaluation, and sustained use by others A sustained record of company-, organization-, or cross-pillar scientific impact, including shaping technical direction, overseeing larger projects, and leading other senior scientists through influence and delegation Strong judgment, independence, and collaborative communication, including experience making recommendations to C-level executives, explaining uncertainty and tradeoffs, and aligning partners on priorities and decisions While it's not required, it's an added plus if you also have: Experience with human labeling, automated judge validation, conversational data, or multimodal AI and machine learning Experience building interactive applications, lightweight interfaces, APIs, or reusable open-source tools Cloud and data infrastructure experience; familiarity with Snowflake, AWS, Dagster, dbt, or Hex is useful, but no particular vendor or framework is required An MS or PhD in machine learning, computer science, statistics, or a similar quantitative discipline; graduate research, publications, or equivalent applied depth At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you're excited about this role but your past experience doesn't align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Pay Transparency Disclosure Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. Figma offers equity to employees, as well as a competitive package of additional benefits, including health, dental, and vision coverage; retirement benefits with company contributions; parental leave and reproductive or family planning support; mental health and wellness benefits; and paid time off. Figma provides paid sick leave, holidays, and other leave benefits in compliance with applicable federal, state, and local laws, including the requirements of the Washington Minimum Wage Act and related regulations. Exempt employees are eligible for employer‑provided paid flexible PTO in addition to flexible paid sick leave. PTO is subject to manager approval. Additional benefits may include company recharge days, cell phone and home internet reimbursements, and a number of lifestyle spending accounts. Figma also offers sales incentive compensation for most sales roles and an annual bonus plan for eligible non-sales roles. All compensation and benefits are subject to applicable plan terms and may be modified by Figma at any time, consistent with applicable law. Annual Base Salary Range: $235,000 — $376,000 USD At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status , or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com . These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities. Examples of accommodations include but are not limited to: Holding interviews in an accessible location Enabling closed captioning on video conferencing Ensuring all written communication be compatible with screen readers Changing the mode or format of interviews To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding. By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice . Application limits: To help candidates focus on the opportunities most relevant to their skills and experience, Figma limits the number and frequency of applications. These limits apply to all roles: Candidates may submit up to 5 applications during any rolling 30-day period. Candidates may not reapply to the same role while an application is active or within 90 days of receiving a rejection. Once the applicable days has passed, candidates are welcome to apply again.

Both roles are for engineers who are comfortable working beyond isolated development tasks: building and improving data pipelines, dealing with performance and reliability, maintaining data quality, and contributing to migration and modernization work. 3+ years of experience, with Apache Airflow, Spark/PySpark, Snowflake, Kafka, AWS S3, Iceberg, and Parquet.

Задачи: проектировать, разрабатывать и поддерживать масштабируемые и надёжные пайплайны и ETL/ELT‑процессы; оптимизировать решения для хранилища данных;

Location: London Workplace: OnSite Company Description We’re http://checkout.comCheckout.com http://Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day. We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers. Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale. If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact. With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started. The role As a Data Science Manager in the Payments Performance pillar you will lead a team of smart and ambitious data scientists that drive the success of our machine learning solution for payment optimization. We are looking for someone passionate about building high-performing teams that consistently deliver value in a rapidly evolving area. You’ll be working with data scientists, analysts and engineers on creating technical solutions and communicating effectively across teams and senior leadership. Your leadership will be pivotal for translating complex problems into actionable roadmaps that ultimately impact millions of online shoppers worldwide.   How you’ll make an impact - Drive the roadmap and priorities for your team, and work with many leaders across the company to enhance our ability to deliver best-in-class performance, through ML. - Build the brain behind mission-critical products that make real-time decisions during the payment processing flow. - Collaborate with stakeholders across the organization such as engineering, analytics, operations and product. - Manage a high-performing team of data scientists, supporting them to achieve a high level of technical excellence and advance in their careers.   What we’re looking for - You have at least 2 years of direct management experience leading data science and ML teams in a complex domain. - Strong background as a Data Scientist (5+ years of proven experience) - You’ve managed teams that have built and shipped machine learning systems and data products at scale. - You work very well cross-functionally, and are able to think rigorously and make hard decisions and tradeoffs. - You thrive on a high level of autonomy and responsibility. - You foster a healthy, inclusive, challenging, and supportive work environment. - Growth mindset, always on the lookout for stretch challenges. - Excellent communication skills, ability to simplify machine learning concepts and data analyses for broad audiences. - Experience dealing with data from complex domains (FinTech, Fraud Detection or Payments Processing are a significant plus) - A PhD or MS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering) is considered a plus. Additional Information Bring all of you to work We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one. Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands. We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here. It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable. Life at Checkout.com http://Checkout.com We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection. Curious about what it’s like to be part of our team? Visit our Careers Page https://www.checkout.com/careers to learn more about our culture, open roles, and what drives us. For a closer look at daily life at Checkout.com http://Checkout.com, follow us on LinkedIn https://www.linkedin.com/company/checkout/life/ and Instagram https://www.instagram.com/checkout_com/

Workplace: Удаленно Объединённая компания Wildberries и Russ — это международная технологическая компания, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. Мы — формирующаяся команда дата‑аналитики (BI) проекта кросс‑бордер бизнеса RWB. Это один из периметров роста компании: впереди масштабирование ассортимента, географии и операционных процессов, а значит — критически важны прозрачные данные, быстрая отчетность и аналитика, которая превращает сырые логи в точки роста. Наша цель — построить систему аналитики и BI, которая станет единым источником правды для продуктовой, операционной и управленческой команд и будет масштабироваться вместе с бизнесом. Обязанности Спроектировать и реализовать архитектуру аналитического стека (DWH/витрины/пайплайны) для высокой производительности и надежности аналитического контура; Организовать загрузку, трансформацию и обслуживание данных в связке Airflow + Hadoop + Trino + ClickHouse; Построить систему отчетности по направлениям: продуктовая, операционная и управленческая аналитика; Сформировать дерево метрик (от стратегических KPI до опережающих индикаторов), описать логику расчетов и обеспечить их согласованность во всех отчетах; Проводить глубокий анализ больших объемов данных, выявлять аномалии, зависимости и скрытые драйверы роста; Формулировать гипотезы, проверять их (включая A/B‑тесты) и готовить понятные выводы и рекомендации для продукта и операционных команд. Требования Продвинутый SQL (оконные функции, CTE, оптимизация запросов) и опыт работы с большими объемами данных; Практический опыт работы со стеком Hadoop/Trino + ClickHouse (или аналогичными MPP/OLAP‑системами); Опыт работы с Airflow (или аналогичными оркестраторами): написание DAG'ов, мониторинг, обработка инцидентов; Опыт работы с BI‑инструментами (Power BI, Tableau, Superset или аналоги), желательно SQL‑first подход; Основы статистики и опыт проведения A/B‑тестов (дизайн эксперимента, проверка гипотез, интерпретация результатов); Умение писать эффективные запросы, профилировать и оптимизировать расчеты в Trino/ClickHouse, понимание принципов партиционирования, сортировки и индексов. Условия Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое; Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании; Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.; Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников; Скидки и партнерские программы: на обучение, страхование, покупки и многое другое; Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.

Location: Singapore; Philippines; Albania; Ukraine; Indonesia; Australia; Serbia; Thailand; Khazakstan; Armenia; Türkiye; Romania Workplace: Remote Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones) Start Date: ASAP Languages: English Team: Engineering   ABOUT THE ROLE Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand. We are looking for a Research Engineer, Synthetic Data to build the synthetic data pipeline. You will turn domain-specific workflows into synthetic training tasks that are realistic enough to teach useful behaviour, structured enough to generate at scale, and difficult enough to expand model capabilities. This is a hands-on research engineering seat, not a commercial or privacy-systems role. You will design how synthetic tasks are created, mutated and validated, and you will analyse what those tasks actually teach models and agents. This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring.   WHAT YOU'LL DO - Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains. - Design synthetic task-generation methods that produce diverse, realistic and learnable tasks. - Build systems and tooling to mutate, validate and improve synthetic tasks. - Analyse model and agent performance on synthetic tasks to understand what the tasks are teaching and where they fail. - Develop metrics to quantify and understand synthetic task diversity, realism, learnability and related qualities. - Take synthetic data pipelines from early research through to reliable systems, including in areas where the roadmap is not fully prescribed.   WHAT WE'RE LOOKING FOR - Proficiency in Python, Docker and Linux environments. - Experience with synthetic data research methods — please be ready to describe this in your application. - A strong understanding of what “good synthetic data” means, and of its limitations. - Experience building synthetic data pipelines end to end, including where there was no fully prescribed roadmap. - Experience working on environments, evals or benchmarks. - Clear written and spoken English, and comfort collaborating remotely across time zones. - Strong candidates may also be detail-oriented enough to catch subtle inconsistencies or edge cases in synthetic data; able to reason from first principles about task design, scoring and failure modes; and comfortable in unstructured problem spaces or early-stage environments. - Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above.   WHY THIS ROLE MATTERS FOR YOU - Build the synthetic data pipeline: You will design the tasks and systems that generate training data at scale, not only run experiments on someone else's pipeline. - Research that has to work in production: Turn messy, domain-specific workflows into tasks that are realistic, measurable and usable for training agents. - See what the data actually teaches: Analyse model and agent performance, and improve the tasks when they fail. - High ownership, unstructured problems: You will build without a fully prescribed roadmap, from method design through tooling and metrics. - San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring.   Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

Location: United States; Colombia; Sacramento; Los Angeles; San Francisco; Canada; Costa Rica; Mexico Workplace: Remote Location: San Francisco or Singapore (on-site), or remote (Singapore or US Pacific time zones) Start Date: ASAP Languages: English Team: Engineering ABOUT THE ROLE Our client is a fast-growing AI company building reliable, fair and open infrastructure for AI training data. Their marketplace helps the people who create data get value for it, and gives AI labs trustworthy data to train on. It is already used by startups, Fortune 500 companies and frontier labs, and the company is backed by leading venture investors, including Y Combinator. They are scaling quickly to meet strong demand. We are looking for a Research Engineer, Synthetic Data to build the synthetic data pipeline. You will turn domain-specific workflows into synthetic training tasks that are realistic enough to teach useful behaviour, structured enough to generate at scale, and difficult enough to expand model capabilities. This is a hands-on research engineering seat, not a commercial or privacy-systems role. You will design how synthetic tasks are created, mutated and validated, and you will analyse what those tasks actually teach models and agents. This is a full-time role, starting ASAP. Offices are in San Francisco and Singapore; remote candidates are welcome if their working hours can overlap about 70–80% with either San Francisco (US Pacific) or Singapore business hours. The exact daily schedule will be agreed during hiring. WHAT YOU'LL DO - Work with subject-matter experts to create synthetic tasks for training AI agents across a range of professional and technical domains. - Design synthetic task-generation methods that produce diverse, realistic and learnable tasks. - Build systems and tooling to mutate, validate and improve synthetic tasks. - Analyse model and agent performance on synthetic tasks to understand what the tasks are teaching and where they fail. - Develop metrics to quantify and understand synthetic task diversity, realism, learnability and related qualities. - Take synthetic data pipelines from early research through to reliable systems, including in areas where the roadmap is not fully prescribed. WHAT WE'RE LOOKING FOR - Proficiency in Python, Docker and Linux environments. - Experience with synthetic data research methods — please be ready to describe this in your application. - A strong understanding of what “good synthetic data” means, and of its limitations. - Experience building synthetic data pipelines end to end, including where there was no fully prescribed roadmap. - Experience working on environments, evals or benchmarks. - Clear written and spoken English, and comfort collaborating remotely across time zones. - Strong candidates may also be detail-oriented enough to catch subtle inconsistencies or edge cases in synthetic data; able to reason from first principles about task design, scoring and failure modes; and comfortable in unstructured problem spaces or early-stage environments. - Technical aptitude and learning potential matter more than years of experience. Motivated candidates are encouraged to apply even if they don't meet every point above. WHY THIS ROLE MATTERS FOR YOU - Build the synthetic data pipeline: You will design the tasks and systems that generate training data at scale, not only run experiments on someone else's pipeline. - Research that has to work in production: Turn messy, domain-specific workflows into tasks that are realistic, measurable and usable for training agents. - See what the data actually teaches: Analyse model and agent performance, and improve the tasks when they fail. - High ownership, unstructured problems: You will build without a fully prescribed roadmap, from method design through tooling and metrics. - San Francisco, Singapore, or remote: Work on-site in San Francisco or Singapore, or remotely, with hours that overlap about 70–80% with either US Pacific or Singapore business hours. The exact daily schedule will be agreed during hiring. Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training. We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.