Washington DC-Baltimore Area
I’m a data analyst who likes solving complicated problems with solutions that feel obvious. I’ve worked in economic consulting and internal audit, using Python, R, SQL, and statistical modeling.
Took a five-month sabbatical to travel and focus on personal and professional growth. Alongside exploring new cultures, I worked on coding projects and advanced my skills in Python, Machine Learning, Version Control, and Automated Testing.
• Designed and implemented statistical models and regression analyses in R and Stata to support antitrust litigation and merger reviews. • Developed scalable data solutions in R and SQL to process and analyze large client datasets, improving efficiency and reproducibility of analyses. • Authored an internal R package to standardize workflows and automate repetitive tasks, reducing analysis time and enhancing consistency across teams. • Mentored and led trainings for junior analysts in statistical modeling, coding best practices, and efficient data manipulation in R, Python, and SQL. • Communicated technical findings to non-technical audiences, including lawyers and regulatory authorities, ensuring clarity and accessibility of complex data insights.
• Designed and executed complex SQL queries to extract, transform, and analyze large-scale client data for litigation support. • Developed automated data pipelines in R and Python to streamline antitrust economic modeling, improving efficiency and adaptability in fast-paced projects. • Created automated workflows for regression analyses, summary tables, and visualizations in R, enhancing reporting accuracy and decision-making.
• Built regression models to estimate overcharges due to cartels, contributing to economic damages analysis that resulted in significant settlements in high-profile cases. • Processed and cleaned large datasets (including 100GB+ files) using R and Python for regulatory compliance and economic modeling. • Developed reproducible workflows for data preparation and analysis, ensuring transparency and accuracy in regulatory submissions.
• Analyzed and cleaned large crime incident datasets using Stata, Python, and Excel to support empirical research on domestic violence trends. • Conducted statistical analysis to examine the effects of COVID-19 on domestic violence and the impact of attending elite universities on lifetime income. • Collected and integrated data from diverse sources, including city police departments, the Facebook Marketing API, and online directories, ensuring accuracy and completeness for economic analysis.
• Developed detailed lessons and taught supplemental content to more than ten students weekly. • Communicated difficult economic and technical concepts in clear, easy-to-understand ways. • Effectively transitioned to teaching online in response to the COVID-19 pandemic.
• Interacted with customers and prepared specialized drinks in a fast-paced environment.