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Portfolio

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

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.

Skills

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

Work experience

Quantitative Researcher
since 11.2022 - Till the present day |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
since 09.2021 - Till the present day |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.

Educational background

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

Additional education

Data Science
03.2020 - 11.2020
Practicum by Yandex

Languages

RussianNativeEnglishUpper Intermediate