About Me
Hi, I’m a Senior Data Scientist/ML Engineer with a firm grounding in statistics and computer science from the University of Waterloo and Georgia Institute of Technology. I have a track record of operating at the intersection of people, business, engineering and science - collaborating with cross-functional teams to design and build high-performance ML systems.
My expertise lies in translating complex business challenges into scalable, data-driven, science-backed solutions that drive measurable business impact and operational efficiency. I specialize in developing machine learning solutions across critical domains including Trust & Safety, fraud detection, content moderation, browser security, search/ranking and financial services.
I’m passionate about building robust ML systems that serve hundreds of millions of users with high availability, leveraging advanced techniques in NLP, search/ranking, distributed systems, and applied mathematics.
- Statistics & Applied Mathematics
- Machine Learning & Deep Learning (NLP, Search/Ranking)
- Distributed Systems & MLOps
Master of Science in Computer Science, 2028
Georgia Institute of Technology
Bachelor of Mathematics, 2019
University of Waterloo
Core Technologies & Skills
NLP/LLM, search/ranking, and distributed ML systems with focus on business impact
Python, SQL for scalable data processing and analysis with statistical and mathematical foundations
AWS, Databricks, Docker, Kubernetes, Terraform for high-performance ML system deployment
Spark, Apache Airflow, dbt, Redis, MongoDB for robust data pipelines and distributed systems
GitHub Actions, CI/CD pipelines, model versioning and monitoring for scalable ML operations
Flask, Django for building production ML services and APIs with high availability
Experiences
• Led end-to-end MLOps Strategy & Cloud Architecture for McAfee’s Search Acceleration initiative (half billion dollar business)
• Designed and deployed high-performance ML infrastructure using Terraform, Databricks Assets Bundle (IaC) and GitHub Actions, reducing model deployment time by over 50%
• Led ML service design and deployment on AWS supporting 200M+ weekly active devices with 99.99% availability worldwide and 95th percentile response time under 100ms
• Led end-to-end research, development, and deployment of novel NLP deep learning services on AWS (ECS, EC2, EMR, SageMaker)
• Achieved under 20ms inference latency per request with 99.99% service availability across global markets for domain name auto-completion
• Delivered 18% uplift in premium domain sales, 16% surge in search volumes, and 30% increase in click-through rates, resulting in $20M+ USD annual SERP revenue increase
• Enhanced performance through personalization based on language, region, and device preferences using FNet, Transformers, BERT, CNN, and bi-directional LSTM architectures
• Led R&D of NLP solution for Canadian Banking Operations to detect crucial payment-related emails from commercial clients nationwide
• Transformed unsupervised text classification into semi-supervised learning paradigm, boosting true positive rates by 19% while reducing false negatives by 35%
• Achieved projected annual savings of $1M CAD through advanced techniques including topic modeling with LDA, XGBoost and BERT embeddings
Certificates
Research/Projects
