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Портфолио

Trading Company

Developing high-frequency trading strategies. - Developed the feature extraction and feature selection pipeline.

Neurus

Developing CV models for production and research uses in various fields - face recognition, image classification, image generation, and character recognition. Developing and maintaining auxiliary software for data collecting and model performance monitoring. - Improved face recognition accuracy from ~94% to 99% by altering the embeddings postprocessing and by using the SOTA embeddings handling framework. - Improved user experience by accelerating SQL procedures during face recognition (the delay became imperceptible for a user). - Implemented automatic collecting of a dataset using the Telegram bot.

Market Research

It is entirely my project I develop myself. By the link, there is only a demo version since I cannot disclose the original project (10k+ lines of code). The demo version contains an exhaustive description, and I am glad to discuss the project, its ideas and solutions, and answer your questions!

Скиллы

Computer Vision
Deep Learning
Docker
Git
Linux
Machine Learning
OOP
Python
PyTorch
SQL
Time Series Analysis

Опыт работы

Quantitative Researcher
с 11.2022 - По настоящий момент |Market Research
PyTorch, LightGBM, Python, Git
This is the project I am developing myself. Developing Data pipelines (feature extraction, feature selection). Developing DL models (designing, training, validation). Developing trading strategies. - Developed a flexible research framework which facilitates conducting experiments on data and models. - Accelerated computing correlation by 4 orders. - Gained basic market predictability which is not a common pitfall and seems to be real.
Computer Vision Engineer
с 09.2021 - По настоящий момент |Neurus
PyTorch, Python, SQL, Git, Docker
Developing CV models for production and research uses in various fields - face recognition, image classification, image generation, and character recognition. Developing and maintaining auxiliary software for data collecting and model performance monitoring. - Improved face recognition accuracy from ~94% to 99% by altering the embeddings postprocessing and by using the SOTA embeddings handling framework. - Improved user experience by accelerating SQL procedures during face recognition (the delay became imperceptible for a user). - Implemented automatic collecting of a dataset using the Telegram bot.
Quantitative Researcher
01.2021 - 02.2021 |Trading Company
LightGBM, Python, Git
Developing high-frequency trading strategies. - Developed the feature extraction and feature selection pipeline.

Образование

Economics with Honours
2008 - 2012
Volgograd State Technical University
Engineering with Honours
2005 - 2010
Volgograd State Technical University

Дополнительное образование

Data Science
03.2020 - 11.2020
Practicum by Yandex

Языки

РусскийРоднойАнглийскийВыше среднего