• Executed and fine-tuned a Retrieval Augmented Generation (RAG) model, to enable global BI teams to efficiently find over 6,000 BI tools and products.• Created an internal tool to automate SOD/EOD balance calculations to refine margin call accuracy.• Contributed to margin collateral optimization by ensuring quality data sourcing and accurate reporting.

• Collaborated in a team of 3 to develop a revenue growth management strategy for Guatemala's leading convenience store.• Utilized Python and SQL for data retrieval and manipulation, alongside Spark and Hadoop for large-scale data processing.• Leveraged Tableau and Power BI in collaboration with marketing and sales departments to develop promotional strategies.• Leveraged Scikit-learn and Pandas in shaping a new pricing strategy grounded in market demand insights.• Incorporated MapReduce and graph-centric analytics, leading to a 30% increase in cross-selling through purchase insights.• Improved pricing based on key value indices, resulting in a 7.5% increase in gross margin and a profit of $4,530,000.

• Analyzed trends in Chicago’s public transportation system, providing actionable insights to anticipate commuter needs.• Leveraged data preprocessing techniques, cleaning, and parsing large datasets to ensure accurate analysis using Pandas.• Created a custom Python tool to transform data from Excel to Word documents, cutting report preparation time by half.• Engineered a program to capture travel time data for driving and public transit during fluctuating peak and off-peak hours.• Integrated a multifaceted travel time testing program, seamlessly consolidating data from four distinct mapping APIs.• Enhanced accuracy in travel time analytics, decreasing calculation errors by 27% between origin and destination centroids.