Our product is a fraud-detection platform, specially designed for the betting business.
We started 2 years ago with a team created from scratch, dedicated processes and technology stack. Currently, we process, store and analyze over a billion events per day using ML and graph algorithms in real-time. We are going to reach two billion events during six to nine months adding more and more detailed data sources.
Unlike the majority of fraud-detection products available on the market, we are not wasting time on no-code/low-code and other marketing staff, focusing on our algorithms and business results instead.
As a part of the Data Science team of Risk Management Stream, you will work with challenging problems using a huge variety of data sources (bets, payments, site behavior data, etc.) detailed, enriched and collected for a long time.
— Innovative solution for fraud detection;
— Real-time prediction;
— Data collection process;
— Strategic view and modelling roadmap.
— Strong knowledge of Python (numpy, pandas, scikit-learn, etc.);
— Experience with big data technologies;
— Experience with at least one DL frameworks (Tensorflow, PyTorch);
— Experience in developing model from scratch (problem definition, data collection, feature engineering, model selection, validation, tuning, etc.);
— Deep understanding of classical ML Algorithms;
— Good knowledge of math and statistics;
— Good knowledge of PostgreSQL, MS SQL.
— Knowledge sharing abilities;
— High level of personal responsibility, readiness to commit on projects instead of tasks;
— Degree in Computer Science, Engineering, Mathematics or a related field.