• I hold a higher technical education.
• I specialize in data analysis with a focus on empowering informed decision-making.
• By extracting insights from complex data sets, I help organizations make data-driven decisions that drive business growth and improvement.

• Programming Languages: Python, SQL (PostgreSQL, MySQL, ClickHouse), NoSQL (MongoDB).
• Data Analysis & Visualization:
  • Libraries: Pandas, NumPy, SciPy, Statsmodels, Pingouin, Plotly, Matplotlib, Seaborn.
  • Tools & Frameworks: Dash, Power BI, Tableau, Redash, DataLens, Superset.
• Big Data & Distributed Computing: Apache Spark, Apache Airflow.
• Machine learning and AI: Scikit-learn, MLlib.
• Time Series Forecasting: Facebook Prophet, Uber Orbit.
• Natural Language Processing: NLTK, SpaCy, TextBlob.
• Web scraping: BeautifulSoup, Selenium, Scrapy.
• DevOps: Linux, Git, Docker.
• IDEs: VS Code, Google Colab, Jupyter Notebook, Zeppelin, PyCharm.

                                 

• Deep data analysis:
  • Preprocessing, cleaning, and identifying patterns using visualization to support decision-making.
• Writing complex SQL queries:
  • Working with nested queries, window functions, CASE and WITH statements for data extraction and analysis.
• Understanding product strategy:
  • Knowledge of product development and improvement principles, including analyzing user needs and formulating recommendations for its growth.
• Product metrics analysis:
  • LTV, RR, CR, ARPU, ARPPU, MAU, DAU, and other key performance indicators.
• Conducting A/B testing:
  • Analyzing results using statistical methods to evaluate the effectiveness of changes.
• Cohort analysis and RFM segmentation:
  • Identifying user behavior patterns to optimize marketing strategies.
• End-to-End Data Pipelines:
  • Building automated ETL processes from databases to dashboards with Airflow orchestration.
• Data visualization and dashboard development:
  • Creating interactive reports in Tableau, Redash, Power BI, and other tools for presenting analytics.
• Web scraping:
  • Experience in extracting data from websites using tools and libraries such as BeautifulSoup, Scrapy, and Selenium for information gathering and data analysis.
• Working with big data:
  • Experience with tools and technologies for processing large volumes of data (e.g., Hadoop, Spark).
• Machine Learning Applications:
  • Capable of building and applying machine learning models for data analysis tasks, including forecasting, classification, and clustering, to uncover deeper insights and enhance decision-making processes.
• Business and Metric Forecasting:
  • Building and interpreting time series forecasts for key business metrics using libraries like Uber Orbit and Facebook Prophet for intuitive, robust forecasting to support strategic planning and goal-setting.
• Working with APIs:
  • Integrating and extracting data from various sources via APIs.
• Process Automation:
  • Automating data workflows and routine tasks using Linux scripting, Apache Airflow and other DevOps tools.

Key Methods: Knowledge Management | Critical Thinking | Research | Content Curation | Information Architecture

A curated knowledge hub demonstrating systematic approach to data analysis, reflecting expertise in structuring complex information and evaluating technical content.

• Systematized 500+ resources into logical learning paths
• Implemented quality control - Selected materials based on accuracy and relevance
• Optimized for usability - Structured content for quick navigation
• Enhanced user experience - Developed a web version to facilitate easy access and navigation
• Synthesized fragmented knowledge into unified framework
• Covered full analytics pipeline from fundamentals to deployment

Stack: Python | ClickHouse | Apache Airflow | Superset | Yandex DataLens | StatsModels | Uber Orbit | Telegram API

Key Methods: A/B Testing | Time Series Forecasting | Anomaly Detection | Cohort Analysis | ETL Pipelines | Dashboard Design

Building analytics process for startup: infrastructure, dashboards, A/B testing, forecasting, automated reports, and anomaly detection.

• Built complete data infrastructure from raw events to automated business intelligence
• Designed interactive dashboards for real-time monitoring of user engagement and retention
• Implemented rigorous A/B testing pipeline with statistical validation of feature experiments
• Developed forecasting models for server load prediction and capacity planning
• Created automated reporting system with daily Telegram delivery to stakeholders
• Established real-time anomaly detection for proactive issue resolution
• Enabled data-driven product decisions through comprehensive analytics ecosystem

Stack: Python | Pandas | Plotly | Tableau | StatsModels | SciPy | NLTK | TextBlob | Sklearn | Pingouin

Key Methods: Time-Series | Anomaly Detection | Custom Metrics | RFM/Cohorts | NLP | Clustering

Comprehensive analysis of Brazilian e-commerce data, uncovering key insights and actionable business recommendations.

• Time-series analysis of sales dynamics, seasonality, and trend decomposition
• Anomaly detection in orders, payments, and delivery times
• Customer profiling (RFM segmentation, clustering, geo-analysis)
• Cohort analysis to track retention and lifetime value (LTV)
• NLP processing of customer reviews (sentiment analysis)
• Hypothesis validation involved conducting tests to verify data-driven assumptions.
• Delivered strategic, data-backed recommendations to optimize logistics, enhance customer retention strategies, and drive sales growth.

Stack: Python | PostgreSQL | Airflow | Yandex DataLens | SQLAlchemy | DBLink

Key Methods: ETL Pipelines | Star Schema | Data Warehousing | Dashboard Design | SQL Optimization

End-to-end data pipeline and business intelligence solution for global distributor Wide World Importers.

• Built automated ETL pipeline transforming OLTP data into optimized star schema data mart
• Designed and implemented interactive dashboard for sales, logistics, and customer analytics
• Developed daily automated data updates with Airflow DAG orchestration
• Enabled data-driven decision making across sales, procurement, and logistics departments
• Reduced manual reporting time through automated data consolidation and visualization

Stack: Python | Pandas | NumPy | SciPy | Plotly | Statsmodels | Scikit-learn | Pingouin | TextBlob | Sphinx

Key Methods: Data Exploration | Statistical Testing | Cohort Analysis | Automated Visualization | Feature Analysis | Machine Learning

Powerful pandas extension that enhances DataFrames with production-ready analytics while maintaining native functionality.

• Seamlessly integrates exploratory analysis, statistical testing and visualization into pandas workflows
• Provides instant insights through automated data profiling and quality checks
• Enables cohort analysis with flexible periodization and metric customization
• Offers built-in statistical methods (bootstrap, effect sizes, group comparisons)
• Generates interactive visualizations with single-command access
• Supports both DataFrame-level and column-specific analysis
• Modular architecture allows extending with domain-specific methods
• Preserves all native pandas functionality for backward compatibility