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.
• 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 simple machine learning models for data analysis tasks, including forecasting, classification, and clustering, to uncover deeper insights and enhance decision-making processes.
• 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.

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

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.

Core Skills: 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 400+ 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