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Регистрация: 08.04.2022

Портфолио

NNFormat

Time series prediction of electric energy consumption of the factories-consumers and pricing on European electricity exchanges. Responsibilities: implementation of predictive models based on Statistical and Machine Learning methods. Predicted results allowed to save ∼600.000 kWh per month.

Lanit-Tercom

Customer churn prediction for mobile operator. Responsibilities: data cleaning and preparation, implementation of Principal Component Analysis, Random Forest and Decision Tree methods (programming in Python and R). Achieved results improved the determination of customer churn increasing the precision from 72% to 80% and were applied in customer retention policy by the mobile operator. Building recognition. Responsibilities: building recognition based on the face recognition algorithm with the use of Smart Augmentation in the computer vision project (Python).

Simble (Sreda Solutions)

I have built a data life cycle: first preprocessing of mobile sensor data, uploading it to cloud services (AWS), segmentation of driving, road features extraction, driving style clustering and risk prediction of a car accident.

Скиллы

Data Science
Docker
ETL
LaTeX
Machine Learning
Mathematical Modeling
Mathematical Statistics
PostgreSQL
Python
R
TensorFlow
Time Series Prediction

Опыт работы

Data Scientist
с 07.2020 - По настоящий момент |Simble
Python, R, PostgreSQL, AWS, MongoDB, Kubernetes
I have built a data life cycle: first preprocessing of mobile sensor data, uploading it to cloud services (AWS), segmentation of driving, road features extraction, driving style clustering and risk prediction of a car accident.
Participant of an expert group
02.2020 - 06.2020 |NTI Center of St. Petersburg Polytechnic University
.
Modeling and comparative prediction of COVID-19 in the World’s and Russian cities. I have developed a mathematical model of epidemic spread in Saint Petersburg.
Data Scientist
07.2019 - 08.2020 |NNFormat
.
Time series prediction of electric energy consumption of the factories-consumers and pricing on European electricity exchanges. Responsibilities: implementation of predictive models based on Statistical and Machine Learning methods. Predicted results allowed to save ∼600.000 kWh per month
Data Scientist
12.2017 - 07.2019 |Lanit-Tercom
.
Customer churn prediction for the mobile operator. Responsibilities: data cleaning and preparation, implementation of Principal Component Analysis, Random Forest and Decision Tree methods (programming in Python and R). Achieved results improved the determination of customer churn increasing the precision from 72% to 80% and were applied in customer retention policy by the mobile operator. Building recognition. Responsibilities: building recognition based on the face recognition algorithm with the use of Smart Augmentation in the computer vision project (Python).

Образование

Applied Mathematics and Informatics (Бакалавр)
2014 - 2018
Saint Petersburg State University

Языки

РусскийРоднойАнглийскийПродвинутый