Rahul Singh
About Rahul Singh
Rahul Singh is a Lead Technical Specialist at the American Chemical Society, where he has worked since 2023. He has extensive experience in machine learning, big data technologies, and data management solutions within the financial sector.
Work at American Chemical Society
Rahul Singh has been serving as the Lead Technical Specialist at the American Chemical Society since 2023. In this role, he focuses on optimizing data processing workflows within the organization. His expertise includes developing machine learning models and implementing Hadoop-based solutions for large-scale data management. Prior to this position, he worked as a Senior Data Engineer at the same organization from 2021 to 2023, where he contributed to various data engineering projects.
Education and Expertise
Rahul Singh holds a Bachelor's degree in Mechanical Engineering from KiiT University, where he studied from 2010 to 2014. His educational background is complemented by extensive professional experience in data engineering and technical leadership. He possesses expertise in integrating Apache Kafka with financial services applications and has played a significant role in enhancing data pipeline efficiency using Apache Spark. Additionally, he has a strong background in deploying Hive for data warehousing solutions.
Professional Background
Rahul Singh's professional journey includes various roles in data engineering and technical leadership. He began his career at Tata Consultancy Services as a System Engineer from 2014 to 2018. He then transitioned to Synechron, where he held multiple positions including Senior Associate Technology, Technical Lead, and Lead Technical Specialist from 2018 to 2020. Following his tenure at Synechron, he worked at airtel X Labs as a Senior Big Data Engineer for a year before joining the American Chemical Society.
Key Contributions and Projects
Throughout his career, Rahul Singh has made significant contributions to data processing and management in the financial sector. He developed machine learning models aimed at optimizing workflows and contributed to the implementation of Hadoop-based solutions. His work also includes enhancing data pipeline efficiency and integrating various technologies to improve data handling capabilities within organizations.