Dmitriy Alekritskiy

Dmitriy Alekritskiy

Mle Team Lead @ Mail.ru Group

About Dmitriy Alekritskiy

Dmitriy Alekritskiy serves as the Mle Team Lead and specializes in stochastic models for financial time series. He holds a Master's degree in Computer Science, Statistics, and Applied Mathematics from Lomonosov Moscow State University and has experience in high-frequency data analysis and machine learning applications in e-commerce.

Work at Mail.ru Group

Dmitriy Alekritskiy serves as the Mle Team Lead at Mail.ru Group. In this role, he focuses on researching stochastic models for financial time series. His work involves applying advanced statistical methods and machine learning techniques to analyze complex data sets, contributing to the company's data-driven decision-making processes.

Education and Expertise

Dmitriy Alekritskiy studied at Lomonosov Moscow State University from 2008 to 2013, where he earned a Master's degree in Computer Science, Statistics, and Applied Mathematics. His academic background provides a strong foundation for his expertise in high-frequency data analysis and machine learning applications, particularly in the e-commerce sector.

Background

Before joining Mail.ru Group, Dmitriy worked at Lamoda Group as a Data Mining Specialist from 2014 to 2017. During his tenure in Moscow, he developed skills in data mining and analysis, which enhanced his capabilities in handling large data sets and extracting valuable insights.

Technical Skills

Dmitriy possesses professional programming skills in R, advanced VBA, and SQL. These technical skills enable him to perform complex data analyses and implement machine learning algorithms effectively. His proficiency in these programming languages supports his research and application of stochastic models in financial contexts.

Research Interests

Dmitriy Alekritskiy has a strong interest in SEM bidding strategies. This interest aligns with his expertise in e-commerce and data analysis, allowing him to explore innovative approaches to improve online marketing performance through data-driven strategies.

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