Position: Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 4

I won an Erasmus grant for the 6-month master's exchange program at the University of Groningen and KPI, where I studied Artificial Intelligence.

 Additional Projects
Deep Learning Creator School Autumn 2021 (in progress) - built fake video classification DL model, mentored by Reface head of ML
Tweet sentiment analysis Spring 2019 - the final project of the course mentored by Grammarly NLP Team Lead, presentation in front of 100 people 
Recommenders System based on Sentiment Analysis 2018 Feb - May- production like architecture to compute recommendations based on reviews 
Handwriting Recognition for Degraded Historical Manuscripts 2018 Apr - July - research in computer vision techniques, wrote the scientific paper 


Certification
Natural Language Processing with Attention Models Coursera 2021 Feb
Fundamentals of Reinforcement Learning Coursera Jul 2021
Natural Language Processing by Projector 2019 Mar - May
Kyivstar Big Data School Course 2017 Nov - Dec
Oracle Database SQL Expert Certificate 2016 Aug
I am a motivated Machine Learning Engineer with mathematical background having 3+ years of experience in tabular data, big data, NLP and CV.

Reface, Data Scientist (Sep 2021 - present) 
dynamic pricing model, recommendation research 

MGID, Machine Learning Engineer in Big Data Team (Nov 2018 - Aug 2021)
Summary 
built ML models for the large volume of data (2T+ daily)
delivered business value and influenced product decisions
conducted researches in the field of NLP and DL
Projects 
Gender classification model - 60% traffic coverage. Wrangled terabytes of data, feature selection on sparse categorical data, implemented neural net 
Identical and similar image finder - 1% growth in revenue estimated with A/B testing. Developed the image similarity system to cluster duplicates or similar pairs with 2000 of new images coming daily 
Classification of video demand availability - got the ensured recall for the positive class of 95%, inference time < 20 ms, ensured that the model works with no degradation in real time 
Image tag multi-label classification - decreased manual tagging by 20 percent. Transfer learning, iterative process with another department.
URL Categoriser research - dataset gathering ideation, hierarchical text  classification models overview, analyzed papers with state-of-the-art methods
User look-a-like clustering - found users who behave like converting customers, approximate nearest neighbor search in the dataset of million samples
I am passionate about Deep Learning and want to work in this domain. I value a challenging enthusiastic and active environment. Previously worked in product companies, I don't want to work at outsourcing.