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


Data Scientist
QSS Analytics

Apr 2022 – Present (5 mos)

• Implementing Machine Learning algorithms on real world dataset 
• Building Neural Networks for Deep Learning Application 
• Data Analysis and Visualization on real world dataset 
• Web Scraping to collect dataset



Python/Big Data mentor
Data Science Academy

May 2022 – Present (4 mos)

Guiding students and maximize their learning through lessons and project work during the Bootcamp program


McKinsey&Company Prohack
This is an anonymized dataset provided by the one of the most prestigious consulting
companies around the world that operate in many countries including Azerbaijan. In this
task, I had to predict the target value (“y”) given all the other features. Throughout this
task, I completed all the advanced necessary steps and all of the necessary phases encompassing the CRISP-DM framework to successfully build an in-house prediction model
for a regression problem. The main objective was to extract new meaningful features from
the existing ones. Target encoder was used for categorical variable transformation. Target encoder is prone to inducing overfitting which was prevented via calling the KFoldTargetEncoderTrain class and removing a Galaxy category from the dataset that do
not exist in the test set. Outliers were handled via IQR method.


Electricity Contract Selection (2022)
This particular project covers step-by-step implementation of advanced Data Cleaning
techniques that are required to get a data to be ready before the Modelling phase in
accordance to the CRISP-DM framework. All steps were completed on Python programming language with the assistance of Pandas, Numpy and Re (Regex) libraries and
their built-in functions. Furthermore, certain functions that allow us to reorganize and manipulate data were compared with regards to computational time to my self-written
functions, because checking time-consumption and computational effectiveness of a code
is often an overlooked aspect of programming. 

Image classification with Python (2021)
Given the Image Classification dataset consists of 6 classes, I am
asked to build a model that can correctly classify images. I applied a
method called transfer learning and tried three neural networks
architectures: VGG16,Resnet50 and Inception-v3 Build a neural model.