• Led two cost optimization projects analyzing $86M in software and IT charges across 150+ tools, collaborating cross
functionally, and presented findings via Tableau Dashboards to directors identifying $8M in realized savings.
• Automated monthly, quarterly, and annual financial processes in Excel using VBA macros, reducing processing time by
60% and streamlining data formatting across datasets.
• Created a VBA and Prompt Engineering guide adopted by 200+ employees to standardize automation practices

• Developed a trading dashboard in Python, Streamlit, and AWS, integrating binomial pricing models for American
options with market comparison, yielding 92% backtesting accuracy.
• Built an ML-based sentiment classifier using scikit-learn, NLTK, and Tensor Flow to analyze news, Reddit, and
Twitter data for real-time market sentiment.
• Applied linear regression and classification models to quantify relationships between news sentiment scores and stock
price movements, improving predictive accuracy by 15%.

• Developed SQL-based ETL pipelines to extract and aggregate historical stock prices, and financial statements,
from multiple databases, reducing data processing time by 50% and ensuring high data accuracy for financial modeling.
• Implemented machine learning models using Monte Carlo simulations and regression analysis to predict stock price
movements, portfolio returns, and risk-adjusted performance, improving model precision by 28%
• Used PCA, correlation analysis, and K mean clustering to optimize data processing, cutting computer load by 40%.

Conducted financial analysis for five SaaS, AI, and fintech companies by creating DCF models with forecasted revenues,
free cash flows, and terminal values using Python and Excel, influencing $11M+ investment decisions.
• Built Python modules with vectorized operations to optimize DCF calculations, increasing efficiency by 37%.
• Presented investment reports to partners, collaborating with three analysts to summarize key valuation metrics, financial
risks, and competitive advantages, guiding decision making for five acquisition strategies.

• Planned and executed four mock competitions, three technical workshops, and two finance conferences with 150+
attendees, resulting in 85 provincial and 12 international qualifiers, while increasing membership by 110%.
• Directed a team of 120+ members and 11 executives for training in finance, marketing, law, and entrepreneurship

Managed financial records using QuickBooks, streamlining transactions and improving accounting efficiency by 67%.
• Conducted weekly inventory audits, identifying and resolving discrepancies, saving the store $2,300 annually.
• Developed annual budgets with 95% forecast accuracy, optimizing resource allocation for a $240,000 operation, and
processed payroll for 6 employees, ensuring 100% on-time disbursements and compliance with tax regulations.

Developed a data collection program using React.js and TypeScript to analyze data from 250+ matches, calculate
scoring metrics, and enable 30+ scouters to input data, optimizing team performance and strategy.
• Led a team of eight students to organize 10+ outreach events, promoting STEM education to 200+ students.
• Created industry-standard part drawings that reduced errors and cut production time by four days.
• Achieved top competition results, placing 3rd in Ontario, 23/3400 globally, and 1st at two district competitions.

Black-Scholes Option Pricing Tool | Python, Streamlit, NumPy, pandas, Matplotlib
• Developed a pricing calculator using Python and Streamlit with a user-friendly interface for inputting key
parameters such as stock price, strike price, time to maturity, volatility, and risk-free rate.
• Designed heatmap visualizations using Matplotlib to show option values for various stock prices and volatilities
• Created configurable ranges for stock price and volatility shocks, to view option pricing sensitivities.

Visually Impaired Assistance App | Python, Swift (IOS), FastAPI, YOLO, Mediapipe, Transformers
• Developed a FastAPI backend in Python, integrating EasyOCR and GPT for 95% accurate text and image response.
• Built real-time object detection with YOLO and Mediapipe, delivering sub-100ms feedback via a Swift iOS app.
• Integrated depth estimation and Google TTS for spatial guidance, increasing user engagement by 60%.
Technologies Used:
cv2, depth-anything, easy-ocr, fastapi, gpt-4, gtts, huggingface, mediapipe, numpy, openai, opencv, pillow, python, pytorch, swift, text-to-speech, ultralytics, vision, yolov8

Console Blackjack Game | Java, Git, IntelliJ IDEA, OOP, JUnit
• Developed a fully functional Blackjack game using object-oriented programming (OOP) principles, with classes for
Deck, Card, and Player, implementing card shuffling, game state management, and player input logic.
• Designed robust input validation with 100% accuracy to handle errors, invalid inputs, and edge cases.
• Optimized game performance supporting up to two concurrent players in a console-based environment, implementing
efficient logic for gameplay flow while maintaining clear, responsive, and user-friendly interfaces.

Long-Term Stock Portfolio Creator | Python, NumPy, pandas, Matplotlib, Yahoo Finance
• Developed a portfolio management tool using Python, analyzing 40+ tickers with financial metrics such as tracking
error, beta values, and correlation, and applying min-max normalization on 3 metrics to optimize performance.
• Built scoring based weighting mechanisms for $1M portfolios, balancing risk/return with quantitative methods.
• Achieved 2nd place in a real-time week-long simulation, tracking the S&P 500 and TSX 60 to a 81% accuracy level.

Market Arbitrage Finder | Python, NumPy, pandas, Matplotlib, Yahoo Finance
• Developed a tool to identify arbitrage opportunities between Canadian and American exchanges.
• Analyzed cross-listed stocks using Matplotlib to find discrepancies by graphing implied vs. actual exchange rates.
• Discussed implications of standard deviation, mean, and median to optimize profits achieving 151% returns.