Experience level:
Education level:
Languages:
Russian - Native language
English - Upper intermediate
Alfa Ecosystem,
Data Scientist (part-time, intern role),
– Data pre-processing, exploratory analysis, and data preparation for ML.
– Using statistical techniques and software tools to interpret data, identify trends,
and generate insights. Developing and validating ML models.
– Collaborating with cross-functional teams.
2024 – present
Tallinn University, School of Governance, Law and Society,
Research fellow (part-time),
– Contributed to two R&D project reports and working paper series.
– Performed scoping reviews and mapping studies using PubMed, Web of Science,
and Scopus databases. Utilized Excel, Rayyan, Zotero.
– Prepared manuscripts for publication in academic journals.
– Presented findings at academic events.
2021 – 2023
Novosibirsk State Pedagogical University, Department of Psychology,
Prae Doc, Post Doc, Docent,
– Analyzed data using StatSoft.
– Successfully defended dissertation in social psychology.
– Supervised research projects for student groups.
– Taught academic courses to student groups.
– Served as an opponent for a Ph.D. dissertation defense.
2013 – 2023
Yaroslavl State University,
Faculty of Psychology,
Degree in Social Psychology,
2019
Yandex EdTech,
Data Science (professional training course),
2024
Data Analysis & Preprocessing: Expertise in cleaning, exploring, and preparing datasets for machine learning using Python (Pandas, NumPy).
Machine Learning: Proficient in building, validating, and optimizing ML models (classification, regression, and time series) with Scikit-learn, TensorFlow.
Data Visualization: Skilled in presenting insights with clear visualizations using Matplotlib and Seaborn.
Collaboration & Tools: Experience working with cross-functional teams and managing projects with Git, Jupyter Notebooks, and Google Colab.
Domain Expertise: Applied ML to real-world problems such as customer churn prediction, game market analysis, and time series forecasting for taxi services.
Research & Statistics: Strong academic research background in data analysis, scoping reviews, and manuscript preparation using SQL, StatSoft, and visualization tools.
Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, SQL, Jupyter Notebooks, Google Colab, Git, NLTK.