Dear candidate, we are a team of experts united by a common passion for artificial intelligence and recommendation systems (RecSys). Our main task is to create a modern, scalable recommendation platform capable of anticipating user expectations and offering personalized recommendations at every stage of their interaction with the Sber ecosystem.
Our solutions cover a wide range of industries, from finance and e-commerce to entertainment and healthcare. If you are close to the idea of participating in the development of a recommendation system that helps millions of users, join us!
Now we are looking for a Junior Data Scientist / ML Engineer for the team!
Technology Stack:
Python, PySpark, PyTorch, Airflow, MLflow, Kubernetes, Redis, Kafka, S3, FastAPI, etc.
The first stage of selection for this vacancy is communication with an AI recruiter. After applying, you will receive an invitation to your email for a preliminary interview with a GigaRecruiter. The conversation will take approximately 10 minutes. Its task is to clarify missing details and speed up the consideration of your candidacy. GigaRecruiter is just beginning its journey, so we ask for your understanding. Your experience and participation will help make it convenient and useful!
Responsibilities
- development and improvement of ML pipelines for RecSys tasks: data preparation, model training, validation, and implementation in collaboration with experienced colleagues
- analysis and work with large volumes of data on PySpark with team support
- study and application of current methodologies in Data Science, Deep Learning, and RecSys
- interaction with the development and analytics team, participation in hypothesis generation and discussion of solutions, presentation of the results of your work.
Requirements
- you are studying in your senior year or have recently completed your studies in a mathematical or IT field
- sincere interest in machine learning and recommendation systems, curiosity, and readiness to learn quickly. We value initiative, relevant questions, and the desire to bring tasks to completion
- practical experience in solving ML tasks in academic, research, or pet projects: data preparation, model training, and quality evaluation
- solid knowledge of Python and familiarity with core libraries for data analysis and ML (NumPy, pandas, scikit-learn; PyTorch will be an advantage)
- ability to independently understand new topics, read technical materials in English, ask questions, and work in a team.
Will be a plus:
- academic, research, or pet project in the field of recommendations
- familiarity with ranking tasks, feature engineering, A/B testing, or ML experiments
- familiarity with PySpark, SQL, Git, Linux, or ML pipeline building tools
- participation in specialized courses, hackathons, competitions, or internships in ML / RecSys
- skills in working with generative AI models
- experience in creating AI agents and using them in work will be an advantage
- experience using GigaChat, Kandinsky, and analogues in products, skills in creating and using AI agents
- instrumental command of AI for analysis, generation, and automation
- experience in team development, participation in open-source, or understanding of code review and testing principles.
Conditions
- hybrid/office work format (optional). The office is located at Nizhny Novgorod, Oktyabrskaya, 35 lit. A
- annual salary review, quarterly and annual bonuses
- corporate gym and recreation areas
- over 400 educational programs from SberUniversity for professional and career development
- adaptation program and supervisor support at the start
- extended VMI (Voluntary Medical Insurance), preferential insurance for family, and corporate pension program
- flexible mortgage discount equal to 1/3 of the Central Bank's key rate
- Prime subscription with the possibility of sharing with three close people
- reward for recommending friends to the Sber team.
