• Customized and maintained the Koha Library Management System, streamlining the process of cataloging various
books, manuscripts, and media, improving data accuracy and accessibility for researchers and archivists.
• Developed automated cataloging workflows to reduce manual data entry time and ensure consistent metadata
standards across the archive.
• Collaborated with team members to optimize search functionality and enhance the user experience for the
digital catalog, making it easier to locate rare and valuable items in the collection.

• Collaborated with a team of software developers to successfully design, develop, and implement a custom
software solution to automate the process of generating EV charger grant applications
• Utilized internal data analysis tools to develop and deploy a location recommendation algorithm to research the
most profitable EV charger locations across Canada
• Developed and maintained a financial tracking system to automate invoice recording and validation to detect
discrepancies in raw material costs for construction projects.

• Built and trained an LSTM model with TensorFlow to predict stock prices based on historical data, improving
accuracy with hyperparameter tuning.
• Reduced prediction errors by 30 % by fine-tuning the model, making it more reliable in changing market
conditions.
• Implemented data preprocessing and feature scaling to handle large stock datasets, enhancing the model’s
ability to generalize across various market conditions.

• Built a full-stack expense tracker with Next.js, React, and Neon, allowing users to easily track spending and
financial data in real-time.
• Integrated OpenAI’s API to provide personalized, AI-driven budgeting tips based on user behavior.
• Streamlined database management with Drizzle ORM, ensuring the app
stays fast and responsive as users log their expenses.

• Built a comprehensive SQL query framework to extract, aggregate, and analyze sales data from Walmart’s
database, uncovering trends and key performance indicators.
• Conducted deep dives into sales patterns by product category, location, and time period, providing actionable
insights for business decisions.
• Boosted query performance by 40% speeding up data analysis and reporting for large datasets.