We are responsible for the operation of risk assessment models, data collection, and lending decisions for SME at Sber.
Responsibilities
Process for ML Engineer:
- analysis and evaluation of incoming technical specifications for the implementation of mathematical models and agents
- communication with Data Scientists on technical specifications, building a model prototype in the Data Laboratory, comparing the prototype with the benchmark
- adaptation of model code for regular execution in PROM, passing IT validation, monitoring the operation of the implemented solution (logging, monitoring)
Process for Data Engineer:
- analysis and evaluation of incoming business requirements for data marts, communication with the client on business requirements
- building a prototype of the designed solution in the Data Laboratory, comparing the prototype with the benchmark, demonstrating the solution to the client
- development of data marts according to the documented requirements and prototype, preparation of accompanying documentation for deploying the solution to PROM, support of the implemented solution (3 LCs).
Requirements
- experience in data engineering/ML engineering from 3 years
- strong proficiency in Python (pandas, numpy, pytorch, sklearn)
- good knowledge of SQL (analytical functions, subqueries, ideally - query optimization)
- understanding of data mart design methodology (ETL)
Will be an advantage:
- experience with the Big Data technology stack (Hadoop, Spark, Hive/Impala)
- knowledge of CI/CD, proficiency in development and infrastructure tools (Docker, Openshift, Jenkins, Kafka)
- experience with LangChain/LangGraph or similar frameworks
- knowledge of the banking subject area
- experience in developing technical documentation
- instrumental use of AI for analysis, generation, and automation.
