Senior Data Scientist with 8+ years building production ML systems that process millions of records at scale. Specialized in fraud detection, risk analytics, and deploying end-to-end ML pipelines. Currently pursuing PhD in Data Science focusing on computer vision applications in sports analytics. First-place winner of MindBridge Analytics Data Science Competition.

Languages & Frameworks

ML & Data Science

Infrastructure & Tools

Real-time player tracking system using YOLO computer vision models for performance analysis. Built automated web scraping pipeline and interactive dashboards for USport basketball statistics.

Stack: Python, YOLO, OpenCV, Oracle Cloud, web scraping, data visualization

Live: sportanalytics.ca

End-to-end ML pipeline processing 200K+ records weekly with 90%+ accuracy. Reduced processing time from 37 hours to 4 hours through GPU parallelization and memory optimization. System identifies fraud indicators including identity theft patterns.

Stack: BERT, Python, AWS SageMaker, SQL, production ML deployment

First-place winner of MindBridge Analytics Data Science Competition. Developed fraud and anomaly detection models for financial datasets, presenting technical methodology to industry expert panel.

Stack: Python, scikit-learn, feature engineering, model interpretability

Built survival analysis and LSTM-based time series models for patent maintenance revenue prediction. Models became critical infrastructure for strategic planning during COVID-19 pandemic.

Stack: Python, LSTM, statistical modeling, time series analysis

PhD in Data Science, Analytics, and Artificial Intelligence - Carleton University (Expected 2027)

• Dissertation: Multi-modal learning for basketball performance analysis using computer vision and physics-informed neural networks

MSc in Statistics - Carleton University (2019)

• Thesis: Statistical assessment of soccer players using association rule mining and neural networks

BSc in Industrial Engineering - K.N. Toosi University of Technology (2014)

Senior Data Scientist & Team Lead - Canada Revenue Agency (2021-Present)

• Led AWS cloud infrastructure adoption across organization
• Built production ML systems processing millions of records
• Conducted fraud investigations using digital forensics
• Present analytical findings to senior government leadership

Data Scientist - Canada Intellectual Property Office (2019-2020)

• Developed forecasting systems for strategic planning
• Recognized with Minister's Award for Excellence and Director General Merit Award

Basketball Analytics Research Lead - Carleton University (2022-2023)

• Led student analysts on computer vision and analytics projects
• Collaborated with coaching staff translating insights into game strategy

• First-place winner, MindBridge Analytics Data Science Competition (2025)
• Minister's Award for Excellence, Canada Intellectual Property Office (2020)
• Director General Merit Award, Canada Intellectual Property Office (2020)
• Nominated for Outstanding Teaching Assistant Award, Carleton University (2023)

Contract Instructor for STAT5703 Data Mining at Carleton University, teaching graduate students machine learning, statistical learning, and data visualization. Active mentor to junior data scientists and student researchers.

Building production data science systems that solve real problems