Data. Product. Impact.
I use data to understand behavior, improve systems, and drive better business outcomes.
What I Do
Data Exploration & Insight Generation
I conduct deep exploratory analysis to uncover patterns, inefficiencies, and behavioral signals within complex datasets.
Predictive Modeling & Machine Learning
I design and optimize predictive models that inform operational forecasting, behavioral prediction, and performance optimization.
Product & Operational Analytics
I'm particularly interested in how data can improve customer experience, workflow efficiency, and revenue performance within product ecosystems.
Business & Strategic Analysis
With a foundation in economics and financial analysis, I evaluate how analytical insights connect to growth, cost structure, and long-term scalability.
Professional Experience
Apple — Specialist Current
Working in a high-performance retail environment strengthens my understanding of customer behavior, conversion dynamics, and operational execution.
I rank among the top sales contributors in the store, contributing significantly to quarterly revenue while maintaining high customer satisfaction metrics. This role sharpens my ability to identify friction points in workflows and understand how systems influence user experience and business outcomes.
PlanetBids — Data Scientist 2022–2023
In this role I learned how data, operations, and strategic decisions intersect in real business environments. I supported data cleaning and analysis initiatives aimed at improving the accuracy and efficiency of government bidding processes.
Through this work, I developed a deeper understanding of how structured data systems influence operational performance and long-term growth. Being involved during the company's acquisition period also gave me insight into financial evaluation, business scalability, and the role of analytics in strategic transitions.
Honda — Machine Learning Consultant & Data Analyst Intern 2021
At Honda, I gained exposure to large-scale operational systems and the complexity of working with Big Data and a variety of machine learning models and techniques.
This experience strengthened my appreciation for operational optimization, cross-functional collaboration, and the role of analytics in improving system-level performance.
Featured Projects
Predicting Vehicle Dwell Patterns for Service Optimization
Problem
Predicting next-location probabilities and dwell times from mobility data was never researched and complex but necessary to build potential on demand vehicle services and repairs.
Approach
Built Markov Chain and KNN-based models using GPS trajectory data and gradient boosting techniques. Conducted feature engineering and spatial clustering to improve predictive accuracy.
Outcome
Improved classification performance through model optimization and identified behavioral dwell patterns.
99PLabs Summer Internship Reflection
Problem
Predicting a used vehicle's accurate condition rating was challenging due to the complexity of historical driving patterns, environmental exposure, and wear-and-tear data.
Approach
Explored and cleaned large datasets capturing driving behavior and vehicle history. Built and evaluated predictive models to estimate vehicle condition scores based on real-world usage patterns.
Outcome
Developed a working model that improved the accuracy of used vehicle ratings, helping inform more reliable assessments for potential on-demand services.
Analyzing the Relationship Between PM2.5 Exposure and Asthma Prevalence Across U.S. States
Problem
Understanding the relationship between particulate matter exposure and asthma prevalence across U.S. states.
Approach
Performed statistical modeling and regression analysis on environmental and public health datasets. Visualized geographic correlations to identify high-risk regions.
Outcome
Identified significant associations between PM2.5 concentration levels and asthma rates, reinforcing environmental-health linkages through quantitative modeling.