Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 1.5


I have almost 1.5 year work experience in data field. First, I started my career as an intern Data Scientist at Unibank, top 4th competitive bank in Azerbaijan in September, 2021. Here my task was to make predictions about the credit scores applying different machine learning classification models such as Random Forest, Naive Bayes, XG Boost, Cat Boost, KNN, SVM, Logistic Regression in Python. Later I had 2 month of experience working as a Junior Business Intelligence Specialist at Retail Sector. I was mainly using T-SQL and Excel, but as the work environment did not get my attention, I decided to continue my career at Kapital Bank, top 1st private bank in the country, where I worked as a Data Analyst at Kapital Bank for 8 months. Here I was responsible for: 
-improving current Card products performance by making data-driven decisions about their current (cashback %, price, miles,
and hearts in Bir Bank) and potential features
-Building interactive dashboards of Card Portfolio and Card Performance in Tableau
-Doing Data Retrieval, Cleaning, Performance Analysis and Scenario Building through effective use of Oracle SQL, Excel, Tableau, and Python. The most challenging and interesting task that I faced during my time here was Cashback Optimization Analysis. In short, I did the analysis of Bir Kart Black Debit card through analyzing 5 months of data about Cashback, Card,
and Bir Bank (mobile application of the bank) usage of customers and prepared 3 EVAM scenarios based on defined 3 segments (nonbirbank users, birbank users but credited cashback only, birbank user and at least 1 time debited cashback. Here I effectively used Oracle SQL and Excel. In June, I got an offer from International Bank of Azerbaijan, the competitive state bank and started to continue my career as Data Scientist again. It has been 3 month approximately so far, but I have done many things since I came because we were about to start a new project called Uplift Modelling together with McKinsey & Company. During this period, I was able to finish Automation of Manual Benefit Calculation of CRM for Uplift Model campaign results through formulating Python script and Data Quality Check automation through writing a class that automatically reads table from Oracle database and does multiple statistical checks like correlation, outlier detection, null check, monthly trend and distribution check. In the meantime, we are building Uplift Models for different campaigns using XG Boost algorithm