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

1) Doing pre-sales prototypes fast during my experience in Perfectial. Sometimes I had only 2 days to do a prototype of a complicated AI project
Industry experience:

1) Internship at R&D department at Abto Software(Aug 2019-Sep 2019)

2) Intern/Junior Data Scientist at Perfectial (Oct 2019-Mar 2020)

Personal projects:

1)  Skin Cancer Lesions classification

In this project classification of skin lesions with 7 different diagnoses was performed using CNN. Also image feature extraction using Radiomics library was performed. 

2) Flappy Bird game bot

In this project we've written a Python library for NEAT(Neuroevolution of augmenting topologies) and also used another Python library for generating a NEAT model for passing Flappy Bird tubes. We've obtained the best result at about 100 tubes passed.

3) Icons generation using Deep Learning

My solution was to train 3 types of models: Variational Autoencoder, Vanilla GAN and DCGAN and compare their results. Using manual evaluation, we can say that results from a Vanilla GAN were not usable, because they lacked sharpness and picked only some very basic patterns. In case of Variational Autoencoder, results were much more icon-like, but too messy. But result of DCGAN is the best of three methods used, because it picked more complicated patterns.

4) Time series analysis and prediction of production in Malaysia

Time series analysis and prediction of production in Malaysia In this project, I tried to predict agricultural production of some products in Malaysia by historical data. The yearly forecast was obtained using several ways: using AR, MA, ARMA and ARIMA models, using classical ML methods like Lasso Regression, but also using Deep Learning technologies like 1d CNN and LSTM.

5) Link between Simpsons sentiment and sentiment in mass media in USA(1990-2017)

In this project, I analyzed sentiment in USA society and compared it with the sentiment in The Simpsons TV show for further insights. The biggest part of the project consisted of data imputation, data gathering, and merging. The main insight was inconclusive. Also, I was trying to find patterns.
I really don't know, I'm open to any new possibilities. But I like new technologies and something experimental.