• Recommendation Systems Development: Designed a cutting-edge offer recommendation engine using advanced ML algorithms, enhancing engagement by 150% for over 13 million loyalty customers across Canadian Tire (CTR), SportChek, and Mark's with hyper-personalized offers.• Customer Engagement Enhancement: Created and deployed predictive models for precise customer segmentation based on engagement potential. Boosted interaction and retention by delivering tailored offers aligned with individual behaviours.• Search Engine Innovation: Created a search engine for relevant offers based on customer queries using advanced embeddings like OpenAI and BERT, and other NLP techniques.• Forecasting & Optimization: Built advanced forecasting models and optimization algorithms to refine offer distribution, significantly improving engagement through accurate forecasting and strategic placement.• Gas+ Campaigns: Tackled declining gas sales at Canadian Tire Gas+ with clustering techniques, resulting in a 49% sales increase through targeted, data-driven campaigns while optimizing budget efficiency.• A/B Testing & Experimentation: Facilitated rigorous A/B testing and experimentation frameworks to validate new features and offers. Enabled data-driven insights through comprehensive analysis of performance metrics and experimental results.• In-House Framework Development: Implemented a scalable in-house framework with Python, PySpark and Airflow, optimized for GCP and on-premises environments, ensuring high performance for advanced machine learning modelling and efficient data pipelining.• Advanced Analytics & Reporting: Created interactive dashboards for real-time analytics and monitoring, delivering actionable insights into offer performance and sales metrics.• Data Migration & Integrity: Oversaw seamless data migration between Hadoop and GCP, ensuring high data integrity throughout the process.• Expertise: Python, PySpark, SQL, Hive, Airflow, Looker, GCP, Azure

• Maintaining pipelines for data processing in Google Cloud Storage (GCP) and AWS S3 buckets using Apache Beam and Boto3 in python.• Preprocessing of raw text, removing noise and assigning labels to the uncategorized training data by making efficient use of regular pattern matching in python.• Developing of Pytorch based Multi-Tasking Deep Neural Architecture using state-of-the-art Word Embeddings, for performing multi-label classification.• Writing scripts to Train, Evaluate and Deploy model on AWS Sagemaker to get online predictions from AWS Lambda in production environment.• Implementing Data Version Control (DVC) to pair model and data with the Git Branch and building a detailed machine learning pipeline.• Reviewing and Analyzing data on BigQuery using SQL queries and monitor machine learning model performance in production on Data Studio.

• Worked as graduate teaching assistant in Tools of Software Engineering Course• Exhibited time management, prioritization & integrity by delivering evaluations and feedbacks on time while pursuing graduate studies.• Wrote marking scripts in python and C++ for grading class assignments.• Held regular programming tutorials for the students

• Provided academic support to undergraduate ECE Students.• Led lab classes ensuring overall comprehension of material covered in the class.• Graded reports and homework assignments.