Sharad Kumar
About Sharad Kumar
Sharad Kumar is a Senior Data Engineer at Expedia Group with extensive experience in data science, software engineering, and machine learning.
Title
Sharad Kumar is currently a Senior Data Engineer at Expedia Group, a position he has held since 2021. He works in the Portland, Oregon Metropolitan Area.
Previous Roles in Data Engineering
Before joining Expedia Group, Sharad Kumar worked at Accenture Interactive as a Data Science Analytics Specialist for 7 months. Prior to that, he served as a Data Architect at Yesler, which was later acquired by Accenture Interactive, for 2 years. His extensive experience in data engineering spans several notable roles, including positions at Nike and Moda Health, where he contributed significantly to the data management and analytics functions.
Experience in Software Engineering
Sharad Kumar has a robust background in software engineering with prior roles at Moda Health as a Software Engineer and at Nike as an Application Engineer. He has also held positions like Technology Manager at Rosetta and Senior Associate at Sapient. These roles have given him a diverse technical skill set that he leverages in his current data engineering work.
Education and Expertise
Sharad Kumar holds two Master of Science degrees from Portland State University; one in Computer Science with a focus on Artificial Intelligence and Machine Learning, and another in Software Engineering. Additionally, he earned his Bachelor of Engineering degree in Computer Science and Engineering from Bangalore University. His academic background lays a strong foundation for his expertise in data science and software engineering.
Professional Skills and Contributions
Sharad Kumar has developed marketing analytics solutions that assist marketers in formulating effective strategies by filtering relevant signals from extraneous data. He has a deep understanding of statistical principles in machine learning, which he utilizes to build predictive models. Furthermore, he applies engineering principles to scale and operationalize these models, ensuring they can be used effectively in real-world applications.