👋 Hi, my name is Maia

🏫 Masters student at the University of CA, Berkeley studying Information & Data Science

🧑‍💻 Come from a background in tech consulting, machine learning, and data science for tech, retail & consumer product industries

🤝🏼 Proven success leading teams, navigating ambiguous challenges, and establishing trust with diverse clients

💻 Looking to collaborate on data science projects centered on privacy and personalized entertainment experiences? Connect with me!

This project involves comprehensive data wrangling, cleaning, and exploratory analysis to uncover key statistical correlations between various factors influencing the revenue of a synthetic medical group. Based on these insights, I developed two distinct and robust machine learning models, decision tree and regression, to predict future revenue with an accuracy margin of 5%.

This project features two statistical models that investigate the relationship between Spotify’s track audio metrics and track popularity using a dataset of over 30,000 songs. Leveraging proprietary Spotify audio features such as danceability, energy, and instrumentalness, initial findings suggest a statistically significant, though modest, relationship, with danceability explaining only a small fraction of the variance in track popularity.

Smart ad bidding is increasingly important in marketing and retail because it leverages automation, data analysis, and adaptive techniques to optimize advertising spend and performance in real time. Inspired by multi-armed bandit literature, this project features an ad auction model and adaptive algorithm designed for multiple instantanous bidders and companies to maximize payoffs.

This analysis seeks to explore the following critical research question: Do Democratic or Republican voters face greater challenges in the voting process? By gaining deeper insights into the specific obstacles encountered by voters from each party, this study aims to inform the development of targeted interventions that can effectively reduce barriers to voting, enhance voter participation, and promote a more inclusive and equitable electoral system for all citizens.

This project conducts a comprehensive analysis of the World Bank's World Development Indicators to address a pivotal research question: How is primary school enrollment associated with labor force participation and unemployment rates across low-, middle-, and high-income countries, and does this relationship vary by gender? The study seeks to uncover the broader socio-economic associations, offering valuable insights for policy development aimed at fostering gender-inclusive economic growth.

• Methodologies: Machine Learning, Data Algorithms, Time Series Analysis, Statistics, A/B Testing and Experimentation Design, Big Data Analytics, Data Visualizations
• Languages: Python (Pandas, Numpy, Scikit-Learn, Scipy, Matplotlib), R (Dplyr, Tidyr, Caret, Ggplot2), SQL, HTML
• Tools: PowerBI, Tableau, Git, Amazon Web Services (AWS), MS Excel

• PMP: Project Management Professional
• AI-900: Microsoft Azure AI Fundamentals
• PL-300: Microsoft Power BI Data Analyst Associate
• 32-Week Training for Core Data Science & Machine Learning Principles, some certificates include
  • Generative AI for Data Scientists
  • Machine Learning with Tree-Based Models
  • Supervised Learning with Skikit-learn
  • Prompt Engineering
  • Extreme Gradient Boosting with XGBoost