Dear Candidate,
We are a team of experts united by a common passion for artificial intelligence and recommendation systems (RecSys). Our main goal 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 drawn to the idea of participating in the development of a recommendation system that helps millions of users, join us!
We are currently looking for a Junior Data Scientist / ML Engineer for our team!
Tech Stack:
Python, PySpark, PyTorch, Airflow, MLflow, Kubernetes, Redis, Kafka, S3, FastAPI, etc.
The first stage of selection for this vacancy is an interview with an AI recruiter. After applying, you will receive an invitation to your email for a preliminary interview with GigaRecruiter. The conversation will take about 10 minutes. Its purpose is to clarify missing details and speed up the consideration of your candidacy. GigaRecruiter is just starting out, so please be 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 deployment in collaboration with experienced colleagues
- Analysis and work with large volumes of data using 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 work results.
Requirements
- You are currently studying in your senior years or have completed your education in a mathematical or IT-related 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 problems in academic, research, or pet projects: data preparation, model training, and quality evaluation
- Solid knowledge of Python and familiarity with the main 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 creating AI agents and using them in work will be an advantage
- Experience using GigaChat, Kandinsky, and similar tools in products, skills in creating and using AI agents
- Instrumental proficiency in 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 35 Oktyabrskaya, lit. A, Nizhny Novgorod
- Annual salary review, quarterly and annual bonuses
- Corporate gym and rest areas
- Over 400 educational programs from SberUniversity for professional and career development
- Adaptation program and supervisor support at the start
- Extended VHI (Voluntary Medical Insurance), preferential insurance for family, and a 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 ones
- Reward for recommending friends to the Sber team.
