Business Analytics
MS Business Analytics student in New York, interested in how data, analytics, and machine learning can improve real-world decisions. Excited to continuously learn new tools and methods to deepen my expertise.
I'm a Business Analytics Master's student in New York, focused on applying data, machine learning, and technology to business decision-making. I work as a teaching and research assistant in business analytics and information technology, helping students build quantitative and programming skills and supporting faculty research with data-driven decisions.
My path has been global: from studying analytics and industrial management in South Korea to earning an engineering and business degree at a leading technical university in Russia, and now continuing my journey in the U.S. This mix of experiences helps me approach problems from different perspectives and adapt across industries and cultures.
I work with Python, R, SQL, and Excel on projects in data analysis, risk assessment, and machine learning. Recently, I've been researching ML applications in cybersecurity, analyzing cryptocurrency return distributions, and studying yield curve dynamics.
St. John's University, Peter J. Tobin College of Business
New York, NY
GPA: 3.96 / 4.0 • Expected May 2026
Bauman Moscow State Technical University (BMSTU)
Moscow, Russia
GPA: 4.99 / 5.0
Korea University of Technology and Education (Koreatech)
Cheonan, South Korea
Global Korea Scholarship (GKS)
GPA: 4.39 / 4.5
As a graduate assistant, I support faculty-led financial research through advanced data analysis and statistical modeling while teaching undergraduate students fundamental statistical concepts and Excel applications. My work spans cryptocurrency market analysis, yield curve forecasting, and AI-enhanced analytics.
Key Contributions:
Tools & Technologies: Python, R, Excel, OpenAI APIs, Statistical Modeling
Worked as a data analytics intern supporting multiple internal bank departments as clients, including Business Intelligence, Machine Learning, and Operations teams. The role focused on translating business questions into data requirements, extracting datasets from multiple sources, and ensuring data quality throughout the analytics process.
Key Contributions:
Tools & Technologies: SQL, Excel, Jira, Confluence, Data Validation, Dashboard Development, Process Documentation
Python, Scikit-learn, GridSearchCV, SMOTE
Built and tuned machine learning models to predict late deliveries using shipping data. Evaluated performance with cross-validation and provided recommendations for carrier selection.
Python (Pandas, NumPy, Matplotlib), Excel
Analyzed membership behavior patterns and retention risks. Created classification models and dashboards supporting targeted outreach for the Institute of Internal Auditors NY Chapter.
R (time-series, regression)
Developed econometric framework to study U.S. Treasury yield curve dynamics using macroeconomic drivers including money supply, inflation, and federal funds rate.
Python (Pandas, SciPy, Matplotlib)
Analyzed historical cryptocurrency returns, constructed distribution plots, and applied normality tests to identify fat tails with implications for risk management.
R, Bloomberg Terminal
Examined monthly returns of S&P 500, oil, gold, and currency indices (1983-2023) using time-series analysis and correlation matrices for portfolio insights.
Analyzed membership behavior patterns and retention risks. Built classification models and dashboards to support targeted outreach and improve member engagement.
Master's Thesis (In Progress)
Developing a machine learning framework using log sequences to predict probability, severity, and timing of cyberattacks. Comparing traditional classification with survival-aware sequence models.
Processes (MDPI), 2023, 11(1), 48
Co-authored research analyzing behavioral models for transportation in smart city ecosystems,
featuring case studies from Germany and South Korea.
View publication →
St. John's University
Korea University of Technology and Education
Full scholarship for exchange program in Industrial Management
All-Student Moscow Conference
Spring 2024 • Awarded for a research paper on a mathematical model of innovation distribution
Bauman Moscow State Technical University
GPA 4.99/5.0 in Engineering Business and Management
Covered advanced modeling techniques for time-dependent data and event-time analysis, including time series forecasting concepts and survival analysis frameworks. Emphasized practical implementation and interpretation for real-world analytical problems.
Skills: Time Series Analysis, Survival Analysis, Statistical Modeling