Shreeya Jaiswal
About Shreeya Jaiswal
Shreeya Jaiswal is a Data Scientist at Noodle.ai in Bengaluru, India, where she has worked since 2022. She holds a Bachelor of Engineering in Computer Engineering from Savitribai Phule Pune University and a Postgraduate Degree in Data Science from IIT Guwahati.
Work at Noodle.ai
Shreeya Jaiswal has been employed at Noodle.ai as a Data Scientist since 2022. In this role, she has contributed to enhancing forecasting accuracy and efficiency through the implementation and optimization of demand models for clients. Prior to her current position, she served as an Associate Data Scientist at Noodle.ai from 2021 to 2022. During her tenure, she provided valuable feedback that contributed to the improvement of Noodle's Demand SaaS product and engaged in the complete project lifecycle, from data analysis to model deployment.
Education and Expertise
Shreeya Jaiswal holds a Bachelor of Engineering (B.E.) in Computer Engineering from Savitribai Phule Pune University, where she studied from 2012 to 2016. She furthered her education by obtaining a Postgraduate Degree in Data Science from the Electronics & ICT Academy at IIT Guwahati, completing her studies from 2020 to 2021. Her academic background provides her with a solid foundation in both engineering and data science, equipping her with the necessary skills for her roles in data analysis and modeling.
Background
Before joining Noodle.ai, Shreeya Jaiswal worked at Infosys as a Senior System Engineer from 2016 to 2020. In this position, she developed her technical skills and gained experience in system engineering. Her transition to data science began with her role at Noodle.ai, where she initially worked as an Associate Data Scientist, allowing her to focus on data-driven solutions and model optimization.
Achievements
Shreeya Jaiswal has successfully implemented and optimized demand models that have improved forecasting accuracy for clients. She utilized an ensemble approach to address model flatness issues, demonstrating her ability to tackle complex data challenges. Additionally, she generated new features that enhanced model learning capabilities, contributing to the overall effectiveness of the data models she worked on.