Sber is looking for a Chief Data Scientist with deep knowledge in AI solution development and experience leading a team of data analysis and machine learning (ML) specialists.
The team is focused on creating tools and solutions for internal security based on AI: developing and advancing AI agents, launching local LLM instances, and fine-tuning them. The scope is broad, ranging from developing DL models for scoring and behavioral modeling to AI agents.
Responsibilities:
- Strategic leadership of the DS domain: researching, evaluating, and implementing new technologies (ML/DL/LLM, agents), shaping the technological vision for AI development within the unit.
- Negotiating with adjacent teams and partners within the block and the bank: defending the domain's interests, establishing effective exchange of approaches and solutions, aligning priorities and resources without compromising quality.
- Representing the unit in developing and approving the set of documents required for model lifecycle support.
- Ensuring the development, implementation, monitoring, and validation of models in accordance with the current modelling governance framework (participating in its development from the unit's perspective and applying it in its processes).
- Representing the unit in developing and approving unified model development standards, policies, and processes (Modelling Governance Framework).
- Identifying priority business tasks for the Block in terms of AI, and being responsible for setting financial and non-financial goals jointly with the block's divisions.
- Collaborating with various business units to identify their needs for AI services, analytics, or data and developing appropriate solutions.
Requirements:
- Over 5 years of experience in machine learning.
- Practical management experience of over 2 years.
- Strong stakeholder management skills and ability to work with "difficult" internal clients.
- Systemic thinking: ability to prioritize under competing demands.
- Experience in successfully creating and deploying AI agents using the LangChain framework or similar tools.
- Good knowledge of Prompt Engineering techniques, structured outputs, and tool usage (Function Calling).
- Practical experience in implementing Retrieval Augmented Generation (RAG) and understanding the benefits of this method.
- Training and fine-tuning of NLP models (SSL, SFT, PEFT): BERT, RoBERTa, XLNet, LLaMA), including independent development of training in PyTorch.
- Experience in configuring and operating local LLM installations and understanding the differences between them (LLaMA, Qwen, DeepSeek, etc.) will be an advantage.
- High productivity and ability to quickly learn new directions and approaches.
- Ability to take responsibility for decisions made and a commitment to continuous professional development.
Conditions:
- Comfortable modern office near Kutuzovskaya metro station (for Kutuzovsky 32 office).
- Work format – office.
- Annual salary review and annual bonus.
- Corporate gym and recreation areas.
- Flexible discount on mortgage loans – up to 1/3 of the Central Bank's key rate.
- Over 400 educational programs at SberUniversity for professional and career growth.
- Extended voluntary medical insurance, preferential insurance for family, and a corporate pension program.
- Free SberPrime+ subscription, discounts on products from partner companies.
