• Developed an alert system for clients with fraudulent behavior by employing panel-data analysis, reducing the number of generated false positive cases by 50%, leveraging the LGBMClassifier alongside extensive data engineering;• Development of a model for the customer churn problem, studying panel data and corresponding time series techniques;• Development and writing of documentation for the bank's risk portfolios according to IFRS9 standards;• Utilising Python, Pandas, Numpy, Scikit-Learn, Tensorflow and MS Azure Cloud for data engineering and modeling;• Implementing full ML projects specific cycle: data gathering, including data cleaning and preprocessing, feature engineering, feature creation to model selection and optimization using advanced hyperparameter tunning methods;• Research activity to gather information about the state-of-the-art workarounds in the domain;• Implementing both classical Machine Learning and Deep Learning techniques for classification problems.

• Software Unit Testing in conformity with CENELEC 50128 standard;• Writing code for creating bugs;• Tests implementation using both BlackBox and WhiteBoxmethodologies;• Use VectorCAST with C programming language to obtain 100%coverage for the components I worked on;• Understanding the code and the development workflow;• Training new team members;• Bug fixing and writing code to check the expected behavior of thecomponents.