We are a team creating a comprehensive fraud detection system and ensuring the online/offline security of millions of bank clients 24/7/365, providing in-house developed tools for:
- Building a connection graph (allows analysis not only of objects and their properties, but also their connections with other objects of various levels and characteristics, and is used to gain additional knowledge about an object by extracting knowledge about its environment, identifying the most significant information, centers of influence, groups of connected objects, and chains)
- Geospatial analytics: location analysis, route planning, and heatmaps
- Identifying and analyzing fraud attempts and other anomalies
- A portfolio of technological services for accumulating and processing large volumes of data with complex structures
We are at the forefront of technological innovation, combining AI capabilities, modern technologies, and societal needs. Our AI transformation initiative is a strategic initiative to integrate LLM agents and multi-agent systems into our proprietary analytical platform:
- AI solutions for deep data analysis
- A set of AI Tools giving agents access to platform tools and services
- A proprietary no-code/low-code platform for orchestrating AI agents and automating workflows
- Integration with internal LLM gateways and agent execution environments
- RAG systems for working with corporate knowledge bases and methodologies
Responsibilities
- Analysis of data sources
- Ensuring transparency and accuracy in data collection, reporting, and visualization
- Implementing new integrations and analytical methodologies
- Compiling data from various sources
- Creating ETL processes
Requirements
- Knowledge of SQL: complex queries, analytical functions, understanding of the physical implementation of joins, query performance optimization
- Experience creating readable Jupyter notebooks
- Familiarity with core Python analytical libraries (pandas, numpy, scipy, statsmodels, pychart)
- Understanding of data warehousing principles, approaches to logical and physical model design
- Experience working with GIT
Will be a plus
- Knowledge of the Hadoop ecosystem: HDFS, Spark, Scala
- Understanding of the basics of mathematical statistics and probability theory, ability to apply them in solving analytical problems
- Experience in making data-driven product decisions;
- Knowledge of basic ML approaches and DG (data governance)
- Experience writing technical documentation
Conditions
- Innovative, ambitious projects and tasks that foster development: there is always an opportunity to enhance your skills and grow professionally
- A knowledge-sharing environment – high expertise within the team
- A cohesive team working on common goals and knowing how to relax well
- Our culture is created by employees themselves – we listen to them and help create and maintain interest-based corporate communities
- Stable salary and a pleasant annual bonus
- A modern IT office near Moscow-City, five minutes from the "Kutuzovskaya" metro station, with a fitness room
