Worked as a data/financial analyst in GoTo, an AI-powered booking aggregator that consolidates service requests from platforms like WhatsApp, Instagram, Telegram, ClassPass, Treatwell, and others. The project aimed to simplify and automate service bookings through a smart unified interface.Built a detailed 5-year revenue projection model in Google Sheets, accounting for seasonal growth, churn, subscription mix (monthly, annual, feature-based), retention and cohort behavior.Designed and implemented Python-based forecasting logic (pandas), including client segmentation and cashflow breakdowns.Developed activation and retention funnel analysis, helping identify user drop-off points.Extracted behavioral data using advanced SQL (window functions, CTEs) for product and finance teams.Built real-time dashboards in Metabase for stakeholder visibility on KPIs and forecasts.Conducted manual QA, tested backend APIs via Postman, load-tested workflows using JMeter, and filed bugs in Jira.Results:The model became the financial core of the product, used for strategic decision-making and investor reporting. Helped validate pricing hypotheses, improve revenue planning, and guide team priorities based on actual user behavior.

- Data Integration: Designed a system for merging multiple data sources (API, CSV) to generate detailed and structured business insights.- Batch Processing Implementation: Developed an efficient batch data processing pipeline, reducing computation time and optimizing large-scale data processing.- User Behavior Analysis: Investigated user activity patterns, preset usage, and segmented the audience based on engagement levels.- Subscription Model Optimization Identified factors influencing user churn, analyzed trial and paid subscription impact.- Seasonality Trends Analysis: Evaluated user activity across weeks and months to uncover recurring behavioral trends.- Interactive Dashboard Development: Created Power BI dashboards featuring dynamic filters and real-time reports for enhanced data visualization.- Flexible Handling of Unstructured Data: Transformed generalized information into actionable business insights.Results:The project improved the accuracy of user activity analysis, enabled the flexible monitoring of key business metrics, and automated data processing across multiple sources. Batch Processing implementation significantly accelerated data analysis and streamlined large-scale data handling. The insights provided contributed to enhancing user engagement and retention strategies , optimizing subscription models, and improving overall business performance.Збір та аналіз великих масивів данихРозробка звітів та аналітичних моделейВикористання статистичних методів для інтерпретації даних та виявлення трендівСпівпраця з іншими відділами для збору даних та формулювання висновківВикористання програмного забезпечення для обробки данихПідтримка прийняття обґрунтованих ділових рішень на основі аналізуІдентифікація можливостей для оптимізації процесів та підвищення ефективностіВедення документації та підготовка аналітичних звітівОновлення знань і методів аналізу данихключевыеProgramming & Databases: Python, SQL, MySQL, PostgreSQL, MongoDBVisualization: Power BI (DAX), Tableau, Google Sheets(EXCEL), MetabaseAutomation & Scripting: Google Apps Script, Telebot, APIsData Analysis Libraries: Pandas, Matplotlib, Seaborn, NumpyMapping & Geospatial: Mapbox, GeoPandasBasic Forecasting & ML: Scikit-learn (Regression, Classification), Prophet (intro use)Data Processing: Regular Expressions, Pickle, JSONBusiness Analytics: KPI reporting, churn & retention analysis, ROI metricsWeb3 / defi / blockchain analytics, gpt critical thinking problem - solving cross - functional collaboration business - oriented mindset adaptability continuous learning

Conducted comprehensive data analysis to improve key business metrics such as retention rate, churn rate, CLV, and ARPU, using Python-based data visualization tools like Matplotlib and Seaborn.Developed predictive models with Scikit-learn, leading to a 19% increase in ROI.Led A/B testing to validate hypotheses on customer behavior and business outcomes.Built interactive visualizations to assist in strategic decision-making.Executed hypothesis-driven analysis to enhance customer retention and optimize operational efficiency.Comprehensive Data Analysis: Conducted an in-depth analysis of CRM data to identify factors impacting deal success and customer retention.Optimization of Business Processes: Proposed and implemented strategies to streamline business operations, enhancing efficiency and profitability.Customer Retention Improvement: Identified key customer retention drivers and suggested measures to reduce churn through targeted support and improved service quality.Advertising and Sales Performance: Analyzed advertising channels and sales department metrics, optimizing spending and enhancing conversion rates.Unit Economics Calculation: Calculated and optimized unit economics (ARPU, CLV, CAC) to improve financial outcomes and customer lifetime value.Geographic and Language Analysis: Conducted geographic and language proficiency analysis to adjust marketing strategies for various regions.Predictive Modeling: Developed time series and predictive models using ARIMA, SARIMAX, and Prophet to forecast demand and seasonal trends.Data-Driven Insights: Provided actionable insights for strategic decision-making, leveraging tools like Pandas, Matplotlib, Seaborn, and Scikit-learn.Cross-functional Skills: Collaborated with marketing, sales, and finance teams to ensure data-driven solutions aligned with business goals.

Service Company Performance Analysis and Dashboard DevelopmentAnalyzed the performance of a service company across multiple units, including revenue, cash flow, and workforce.Designed and implemented an automated Power BI dashboard using Power Query for real-time updates and insights from Google Sheets data.Provided detailed visualizations for tracking monthly performance, sales, and transactions, enabling identification of trends and performance optimization.Automated data refreshes, reducing manual data entry and minimizing errors by 70%, leading to a more streamlined process.Recruitment Pipeline Automation and ReportingDeveloped automation for tracking and updating candidate statuses using Google Apps Script, integrated with Power BI for real-time visualization and analysis.Automated daily updates on recruitment data, synchronized across Google Sheets, ensuring accurate status reporting for multiple positions and countries.Provided HR with interactive dashboards for monitoring key metrics such as interview outcomes, time to hire, and candidate progression.Achieved a 50% reduction in manual workload for managers by automating the process of status updates and data synchronization.

Optimized processes for handling large data sets, increasing employee efficiency by 80%, managed financial strategy and analysis, developed and implemented data-driven solutions to improve operational efficiency, collaborated with cross-functional teams to enhance financial reporting and decision-making.

Implemented data analysis techniques to streamline accounting processes, developed interactive financial dashboards, optimized tax reporting procedures, improved data accuracy, fostering stronger relationships with counterparties.