Data Scientist for Risk Management Stream Offline

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.

You will have a direct impact on:

— Innovative solution for fraud detection;

— Real-time prediction;

— Data collection process;

— Strategic view and modelling roadmap.

Essential professional experience:

— 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.

Desirable skills and personal features:

— 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.