Ramit Jaal
About Ramit Jaal
Ramit Jaal is a Senior Data Scientist with extensive experience in data analysis and quantitative research. He has worked at various organizations, including Changing The Present and Natixis Investment Managers, and currently applies his expertise at StatusNeo in India.
Work at StatusNeo
Ramit Jaal has been employed as a Senior Data Scientist at StatusNeo since 2021. In this role, he applies advanced data science techniques to solve complex business problems. His responsibilities include developing predictive models and conducting data analysis to inform strategic decisions. He has contributed to various projects that enhance operational efficiency and data-driven decision-making within the organization.
Previous Experience in Data Analytics
Before joining StatusNeo, Ramit Jaal worked as a Data Analyst at Changing The Present from 2020 to 2021. During his tenure, he focused on analyzing data to support organizational goals. Prior to this, he gained experience as a Quantitative Researcher Co-Op at Natixis Investment Managers in 2019, where he contributed to quantitative analysis and research projects.
Educational Background
Ramit Jaal holds a Bachelor of Technology (BTech) in Mechanical Engineering from Dr. B R Ambedkar National Institute of Technology, Jalandhar, which he completed from 2009 to 2013. He further advanced his education by obtaining a Master of Science (MS) in Data Analytics Engineering from Northeastern University, where he studied from 2018 to 2020.
Technical Projects and Initiatives
Ramit Jaal has led several technical initiatives throughout his career. Notably, he spearheaded a geohash7-level network volume usage forecasting project that improved forecasting accuracy by 15% through the implementation of LSTM with an attention mechanism. Additionally, he managed a project that achieved over a 20% reduction in project execution time by utilizing the DBSCAN clustering algorithm to address quality and coverage gaps.
Sentiment Analysis and Optimization Models
Ramit Jaal has conducted sentiment analysis on customer feedback using a Large Language Model through the LangChain framework. This work aimed to effectively categorize high-priority areas for improvement. He also developed an interest cost optimization model at Jio Financial Service, which optimized fund sourcing strategies using PuLP, resulting in significant interest savings.