Summary
Salil Kapur is a Senior Data Scientist with 11 years of experience applying machine learning, deep learning, and big-data engineering to retail, robotics, and supply-chain problems. He has led teams at Loblaw and delivered production ML solutions—demand forecasting for ~1,200 stores, markdown and replenishment optimization, and Snowflake/Airflow/SageMaker pipelines—while currently optimizing ML deployment and Spark usage at Tiger Analytics. Salil pairs academic rigor (MS in CS, published work and an authored course/book chapters) with hands-on systems engineering, from CUDA-accelerated drone vision on NVIDIA Jetson to cloud deployments on AWS and GCP. He is the author of a popular “Hands-on Deep Learning with TensorFlow” course and a recognized Sympy contributor, reflecting a long-standing habit of turning research artifacts into practical tools. Notably, he has repeatedly shrunk and sped up models for edge deployment (achieving ~99.5% model size reduction) and built end-to-end image pipelines that sustain real-time video at 45 FPS. Based in Canada, Salil combines creative curiosity—sketching and coding as hobbies—with a track record of shipping robust, production-ready ML systems.
11 years of coding experience
8 years of employment as a software developer
MS in Computer Science Data Science Specialization, MS in Computer Science Data Science Specialization at Dalhousie University
Bachelor of Technology (B.Tech.) CSE, Bachelor of Technology (B.Tech.) CSE at Guru Nanak Dev University, Amritsar