Position: Machine Learning Engineer
Category: Data Science
English level: upper
Years of experience: 2.5

1) Took part in the final part of the hackathon and developed a complex system that is able to accurately distinguish and segment as same images of flat or not. Used state-of-the-art models like MobileNet v3, ResNet, CLIP, and YOLOv8 in synergy to get the best score. Placed 5th place out of more than 10 teams;

2) Successfully completed MacPaw Summer Internship 2021 as a Data Science Intern.
1) Building classical Machine Learning models on overly imbalanced data (1: 5000) using the most modern imbalanced learning tools. Effectively serving and building more than 50 models a month. Approaching, model reporting, and communicating with clients.  
2) Efficiently rewrote Pandas data frame preprocessing on Apache Spark, which made preprocessing 3x times faster.
3) Research on current SOTA imbalance learning methods significantly improved the ability of the model to detect the skewed class of the dataset.
4) Currently creating visualization model report with Seaborn, Pandas, and Jinja templates.
5) Worked in a big team of Data Scientists on a model for churn prediction, greatly imbalanced data, and effectively used various tools for dealing with the skewness of classes, which raised the accuracy of the model by 30%.
6) Created a custom template for building machine learning models, that sped up its implementation more than 3 times.
7) Conducted lectures on Machine Learning and Data Storytelling (total number of listeners ~ 150 workers from different departments).
8) Automation of processes with Python, JS, Airflow, SQL databases, Docker, and web-developing tools.
9) Worked, analyzed, and modeled large clusters of data conducting efficient EDA, further target definition, and modeling with various ML models.
10) Fine-tuned RNN for semantic segmentation, used anomaly detection and topic modeling approaches to analyze and process tweets on their emotional color.
11) Utilized the Tableau tool for building efficient reports and presentation skills.
More deep learning utilization preferably in the Computer Vision sphere. More engineering tasks, less Analytics ones. MLops experience and building difficult state-of-the-art models, that will be used in real-world scenarios. Decent financial and social security.