Position: Data Scientist/Engineer
Category: Data Science
Experience: 4 years | English: upper

For the project "Development of a compensation mathematical model and an algorithm for thermoradiation creep for elements of an inertial satellite navigation system", the developed model for taking into account the accumulated damage in the elements of the navigation satellite system made it possible to improve the quality of satellite orientation in orbit by more than 3 times

For the project "Optimization of the analysis system of dealerships of the automotive industry", the developed pipeline allowed to increase the speed of data processing by more than 120 times. DASK, AirFlow, PostgreSQL, Python were used to build the processing pipeline
I am a PHD in Engineering and Mathematics. More than 7 years of experience in building and implementing mathematical and statistical models, 4 years of experience in data analysis, parsing and processing, creation and implementation of machine and deep learning algorithms.

Project name: Development of a compensation mathematical model and an algorithm for thermoradiation creep for elements of an inertial satellite navigation system
Description of the project: according to the requirements of the client, a full cycle of implementation was developed: conducting experiments on thermoradiation creep of elements of a satellite navigation system, developing a mathematical model, creating an algorithm and executing script for the onboard system
The script allows you to collect data, process, assess the damage of the inertial navigation system, make adjustments to the control system
Tools & Technologies: ANSYS, MatCad, MatLab, С++, CUDA
Role: data scientist
Responsibilities: Conducting experiments, developing a mathematical model, creating an algorithm and an executing script

Project Name: Optimization of the analysis system of dealerships of the automotive industry
Project Description: Based on clients data on car purchases and their online activity, a system for processing, analyzing and storing data has been developed. To monitor the activity of clients in the network, a Data Pipeline of transformations was built, based on processing utm-parameters and parsing urls. The system allows us to perform mapping, aggregation, data filtering in parallel mode, to issue recommendations and the best offers in terms of relevance and customer needs
Tools & Technologies: PostgreSQL, pandas, spark, GCP, DASK, multiprocessing. Optimized queries and database architecture
Role: data engineer
Responsibilities: Data processing pipeline development

Project Name: A machine learning model for predicting a company's IPO
Project Description: Based on the database of the Crunchbase service, classification and clustering models have been built, which make it possible to determine the potential and the company's entry into an IPO.
Tools & Technologies: Sklearn algorithms (SVC, logistic regression, random forest, KNN, Affinity propagation), NLP (BERT, FinBert, spacy, QA, TF-IDF), statistical analysis were used to solve the problem.
Role: data scientist
Responsibilities: Building machine learning models for financial data