Shailesh U
About Shailesh U
Shailesh U is a Product Manager at Impact Analytics in Bengaluru, India, with expertise in standardizing and automating reports in the insurance and retail sectors. He has a strong background in predictive analytics and has led teams in decision sciences projects, focusing on improving marketing campaign effectiveness.
Work at Impact Analytics
Shailesh U has been serving as a Product Manager at Impact Analytics since 2021. In this role, he focuses on standardizing, centralizing, and automating business-critical reports, particularly in the insurance and retail sectors. His responsibilities include leading teams and managing decision sciences projects, leveraging his expertise to enhance business functions through data-driven strategies. Shailesh previously held the position of Decision Science Leader at Impact Analytics for 11 months, where he contributed to various analytical initiatives.
Education and Expertise
Shailesh U studied at the Indian School of Business, where he gained advanced knowledge in business management. He also completed his undergraduate studies at Amrita Vishwavidyapeetam from 2010 to 2014. His expertise lies in predictive analytics, particularly in improving marketing campaign effectiveness through customer lapse prediction and next shop predictions. He has developed advanced analytical models aimed at predicting fraudulent claims, showcasing his proficiency in the insurance sector.
Background
Shailesh U has a diverse professional background with significant experience in decision sciences. He began his career at Mu Sigma Inc., where he worked as a Decision Scientist from 2014 to 2017 and later as an Apprentice Leader from 2018 to 2020. His roles involved managing teams and implementing data engineering projects that enhanced business operations. He has collaborated with Fortune 500 companies across Europe and Australia, further solidifying his expertise in data-driven decision-making.
Achievements
Throughout his career, Shailesh U has led teams of over 20 members in various decision sciences projects. His work includes the development of analytical models that predict fraudulent claims and the implementation of data engineering projects to improve business functions. His focus on extracting business value through data-driven strategies has contributed to the effectiveness of marketing campaigns and operational efficiencies in the sectors he has worked in.