Project Overview:

The client is present on the Ukrainian market since 1990.  

The bank offers to its clients qualified financial services in the best European financial traditions. 60% of the bank's shares belong to one of the world's largest financial groups BNP Paribas, 40% - the EBRD. 

The client is trusted by about 2 million customers, 170 thousand SMB companies and 600 groups of corporate companies - leaders of the domestic economy and international corporations. At the service of our partners are about 260 branches and 1000 ATMs throughout Ukraine. 

Рекрутинг лід
Катерина Леспух
  • Data requirements, data source identification, gathering, quality control; might be cleaned and governed data source to complex to access data or non present data that would require to design strategy to acquire (open data or web gathering, data acquisition, new product to propose to gather the data); all this under strict respect of compliance and legal framework (with help of data office);
  • Feature engineering in potentially complex configuration (like transforming time series of events into features);
  • Modelisation: find appropriate modelling strategy (algorithm selection, model architecture, model quality check and analysis, verification of results with business (are feature and tests sound from a business perspective ?); include temporal stability analysis in some cases; include more or less detailed documentation of the model(s);
  • Prototype : organize live tests (A/B testing, light implementation approach), analyse results and confirm economic or business interest of the use case; 
  • Implementation support for IT / business;
  • Innovate and share;
  • Propose new ideas to improve existing use cases or create new one;
  • Run R&D project to explore new ideas or technologies;
  • Follow up trends : academic research in some specific domain useful for the bank;
  • Attend analytics community event and propose content;
  • Team leadership and management of the two data scientist;
  • Project delivery vs businesses;
  • Technical and Scientific referent to diagnose / arbitrate / correct difficult problems and support data scientists.
  • Strong theoretical knowledge on machine learning algorithm (logistic regression, tree based models, ensemble models, boosting techniques, GAM and GLM) and neural network techniques (deep learning with CNN or NLP); should be able to choose appropriate models based on constraints (transparency & explanability, model implementation and available IT environment, available data) and not only on pure performance metrics, and to explain choice and results;
  • 5 years of experience as data scientist or a first successful experience as data science team leader;
  • Upper intermediate level of English / Ukrainian (oral and written);
  • Capacity to quickly understand new business fields and interact with business representatives;
  • Programming experience in python (scikit, capacity to produce industrial code and not only notebooks), development in agile mode with devops, unit testing tools.
Nice to have:
  • Specialization on one field of application (NLP, voice, credit scoring, marketing models, fraud, etc.);
  • Experience in financial services;
  • Curious and passionate for data, mathematics and business;
  • Basic knowledge of regulatory requirement around data, or ability to quickly ramp up on it;
  • More languages : French, Polish, Russian, German, Turkish.

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