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Senior Data Scientist (Applied AI / LLM)
Nebius — это быстрорастущий международный игрок в сфере AI-инфраструктуры с листингом на Nasdaq. Работа над передовыми LLM-проектами в сильной инженерной команде делает эту вакансию крайне привлекательной для опытных специалистов.
Сложность вакансии
Роль требует глубоких знаний в области LLM и RAG, а также 5+ лет опыта. Высокая планка ожиданий по выводу моделей в продакшн и работе с инфраструктурой делает позицию сложной.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции Senior Data Scientist в Тель-Авиве рыночный диапазон составляет 35,000–50,000 ILS в месяц. Nebius, как международная публичная компания, обычно предлагает конкурентоспособные пакеты, соответствующие верхней границе рынка.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Data Scientist position at Nebius. With over five years of experience in applied machine learning and a proven track record of deploying production-grade AI systems, I am excited about the opportunity to contribute to your Applied AI and LLM initiatives in Tel Aviv. My background aligns perfectly with your focus on turning LLM capabilities into reliable business solutions, particularly in building robust evaluation frameworks and agent-based automation.
In my previous roles, I have successfully designed RAG pipelines and integrated LLMs into complex workflows, ensuring high accuracy and production readiness. I am particularly drawn to Nebius's mission of leading the AI cloud economy and your emphasis on real business impact over pure experimentation. I am confident that my expertise in Python, SQL, and MLOps, combined with my experience in predictive modeling for business domains, will allow me to make immediate contributions to your team and help scale your AI infrastructure.
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Откликнитесь в nebius уже сейчас
Присоединяйтесь к Nebius, чтобы создавать передовые LLM-системы в одном из самых быстрорастущих AI-облаков мира!
Описание вакансии
Why work at NebiusNebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field.
Where we workHeadquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with R&D hubs across Europe, North America, and Israel. The team of over 1400 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI R&D team.
The role
Nebius is looking for a Senior Data Scientist (Applied AI / LLM) to design, build, and deploy production-grade AI systems based on large language models. You will focus on turning LLM capabilities into reliable, measurable business solutions, while also contributing to predictive modeling across different business domains.
Your work will include building evaluation frameworks, improving model accuracy, and developing agent-based systems that automate analytical and data workflows (e.g., data quality monitoring). The role emphasizes production readiness, robustness, and real business impact—not experimentation.
You’re welcome to work in our office in Tel Aviv.
Your responsibilities will include:
- LLM system development. Design and deploy LLM-based solutions (e.g., RAG pipelines, agent workflows) for real business use cases.
- Evaluation & reliability. Build evaluation frameworks to measure accuracy, consistency, and failure modes of LLM systems. Continuously improve performance based on real-world usage.
- Agent-based automation. Develop agentic solutions to automate workflows such as data quality checks, anomaly detection, and reporting.
- Applied predictive modeling. Apply classical ML and statistical methods to business domains such as HR and Finance (e.g., forecasting, classification, risk modeling).
- Production & MLOps. Deploy and maintain models in production, ensuring monitoring, versioning, and scalability.
- Stakeholder collaboration. Translate business needs into AI/ML solutions and communicate trade-offs, risks, and performance clearly.
- Cross-domain contribution. Contribute to adjacent areas (e.g., forecasting models) to ensure team redundancy and shared ownership of critical workflows.
We expect you to have:
- Experience as a data scientist or applied ML practitioner (5+ years).
- Experience building and deploying LLM-based systems in production.
- Experience working in production environments with model monitoring and iteration.
- Experience with modern data science and ML ecosystems using Python.
- Experience working with large datasets and strong SQL skills.
- Understanding of LLM evaluation, prompt design, and system behavior.
- Strong foundation in statistics and machine learning fundamentals.
- Demonstrated ability to deliver production-grade AI systems end-to-end.
- Demonstrated ability to translate business needs into AI/ML solutions and communicate clearly.
- Working knowledge of spoken and written English.
It will be an added bonus if you have:
- Experience with RAG architectures and retrieval systems.
- Experience designing or working with agent-based workflows.
- Experience with MLOps tools and production ML systems.
- Experience working in cloud environments (preferably Azure).
What we offer
- Competitive salary and comprehensive benefits package.
- Opportunities for professional growth within Nebius.
- Flexible working arrangements.
- A dynamic and collaborative work environment that values initiative and innovation.
We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us!
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Навыки
- Python
- SQL
- LLM
- RAG
- Machine Learning
- Statistics
- MLOps
- Predictive Modeling
- Azure
- Data Quality Monitoring
Возможные вопросы на собеседовании
Проверка опыта работы с актуальными архитектурами LLM.
Расскажите о вашем опыте проектирования RAG-систем: как вы решали проблему галлюцинаций и какие метрики использовали для оценки качества поиска?
Вакансия делает упор на надежность и бизнес-результат.
Как вы подходите к созданию фреймворков оценки (evaluation frameworks) для LLM, чтобы гарантировать стабильность ответов в продакшене?
В описании упомянуты агентские системы.
Опишите кейс, где вы использовали агентные воркфлоу для автоматизации аналитических задач. С какими трудностями в управлении состоянием агента вы столкнулись?
Роль предполагает работу с классическим ML в HR и финансах.
Как вы выбираете между использованием LLM и классических методов машинного обучения для задач прогнозирования в бизнес-доменах?
Проверка навыков MLOps и инженерной культуры.
Каков ваш процесс мониторинга моделей после деплоя? Как вы организуете цикл итеративного улучшения модели на основе реальных данных?
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