Mansi Raj
About Mansi Raj
Mansi Raj is an Associate Software Engineer (Data Science) - II at HighRadius, specializing in Machine Learning and AI solutions within the B2B FinTech SaaS sector. With a strong academic background in Information Technology and extensive experience in data science roles, Mansi has contributed to significant improvements in AI accuracy and system efficiency.
Work at HighRadius
Mansi Raj currently holds the position of Associate Software Engineer (Data Science) - II at HighRadius, where she has been employed since 2023. Prior to this role, she served as an Associate Software Engineer (Data Science) - I from 2022 to 2023, and as a Data Science Intern from 2021 to 2022. Throughout her tenure at HighRadius, she has focused on implementing Machine Learning and AI solutions within the B2B FinTech SaaS sector, specifically targeting automation for the Office of the CFO.
Education and Expertise
Mansi Raj earned her Bachelor of Technology (BTech) in Information Technology from Kalinga Institute of Industrial Technology, Bhubaneswar, completing her studies from 2018 to 2022. Prior to her university education, she attended Aditya Birla Public School, where she studied the Science Stream and achieved her Higher Secondary School certification from 2017 to 2018. Her educational background has equipped her with a solid foundation in data science and technology.
Background
Mansi Raj began her career in data science with an internship at HighRadius, where she worked remotely from 2021 to 2022. After her internship, she transitioned to the role of Associate Software Engineer (Data Science) - I, before advancing to her current position. Her early education at Aditya Birla Public School laid the groundwork for her interest in science and technology.
Achievements
Mansi Raj has made significant contributions to her team at HighRadius. She played a key role in enhancing the prediction process pipeline, which resulted in a 60% reduction in system overhead time. Additionally, she improved AI accuracy for predicting blocked orders from 15% to 70% and contributed to a 20% increase in accuracy for an AI-based credit limit decisioning model, facilitating the automation of credit review outcomes. Her experience also includes leadership in managing freshers and collaborating with cross-functional teams.