As a data scientist, I specialize in transforming raw data into predictive models and strategic insights that drive business decisions. With expertise in end-to-end data science workflows, I excel at statistical analysis, machine learning implementation, and building scalable data solutions. My experience spans data engineering, advanced analytics, and model deployment across diverse industries, enabling organizations to leverage their data for competitive advantage and innovation.
In the 'LEARN MORE' Tab, you can find my portfolio, which showcases a few of my projects, with the tools and methods used, and their results. You can also find my resume and contact information below.
Learn moreExplore my comprehensive portfolio of data science projects demonstrating end-to-end analytical workflows, from data acquisition and processing to advanced modeling and business intelligence solutions that deliver actionable insights and drive strategic decision-making.
Complete end-to-end data science project for BCG's Data Science Job Simulation, developing a customer churn prediction model for PowerCo utilities company. Implemented advanced feature engineering, Random Forest modeling, and business intelligence to identify retention opportunities worth €21M annually.
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Conducted exploratory data analysis on Netflix's movie catalog using Python, investigating temporal patterns in content duration and genre-based trends to understand evolving viewer preferences in the streaming entertainment industry.
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Built SQL-based analytical framework for COVID-19 data analysis, developing complex queries using Google BigQuery to create summary tables and derive key health metrics including infection rates, mortality percentages, and geographic comparisons.
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Created interactive Tableau dashboards to visualize COVID-19 trends and metrics, designing user-friendly interfaces that enable stakeholders to explore infection rates, mortality data, and geographic patterns during the global pandemic.
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Applied data science techniques to analyze NYC public school SAT performance data, using Python and Pandas to identify top-performing schools, calculate performance metrics, and analyze geographic variations in educational outcomes.
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Built comprehensive data visualization solutions using R and ggplot2, creating publication-quality charts and statistical plots for exploratory data analysis and effectively communicating data insights across different datasets and use cases.
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