Samip Singhal
About Samip Singhal
Samip Singhal is an engineering leader specializing in recommendation systems, currently working at Credit Karma since 2022. He has a background in data architecture and management, with previous roles at Teradata, SocialCode, and New York & Company.
Work at Credit Karma
Samip Singhal has been part of Credit Karma since 2022, serving in Engineering Leadership & Management focused on recommendation systems. In this role, he has played a significant part in scaling the recommendation system to accommodate over 120 million members. His work emphasizes leveraging data and machine learning to enhance user experience and drive financial progress.
Previous Experience in Data Engineering
Before joining Credit Karma, Samip Singhal worked at SocialCode as a Senior Engineering Manager in Ads Data & Analytics from 2020 to 2022. Prior to that, he served as the Manager of Data Engineering and Science at New York & Company from 2016 to 2020. His career began at Teradata, where he worked as a Data Architect from 2012 to 2016 in the San Francisco Bay Area.
Education and Expertise
Samip Singhal holds a Bachelor of Engineering degree in Computer Science from BMS Institute of Technology and Management, where he studied from 2004 to 2008. He furthered his education at Visvesvariah Technology University, also earning a Bachelor of Engineering. Additionally, he completed a program at NYC Data Science Academy, enhancing his expertise in data science and engineering.
Background in Database Engineering
Samip Singhal began his professional journey as an Associate Database Engineer at Tech Mahindra, where he worked from 2008 to 2012 in Mumbai, India. This early experience laid the foundation for his extensive career in data engineering and management, leading to roles that focus on data-driven decision-making and analytics.
Specialization in Recommendation Systems
Samip Singhal specializes in engineering leadership and management with a focus on recommendation systems. His extensive experience includes leveraging data and machine learning techniques to improve financial outcomes for large user bases, demonstrating his capability in driving impactful engineering solutions.