About

Nishant Lalwani is an experienced Big Data Developer with a strong background in data analytics, machine learning, and statistics. He has worked for Tata Consultancy Services, a leading information technology and services giant, and has gained valuable experience working as a developer for a leading US-based banking and financial services client. Nishant's experience has taught him the importance of data in businesses and how technology can help in providing solutions, predicting upcoming challenges, and serving customers efficiently. This realization motivated him to pursue a Masters in Business Analytics with a Data Science concentration at the University of Texas at Dallas, where he is currently enrolled as a graduate student. Nishant's strong points include big data analytics, machine learning, statistics, Apache Spark (Spark Tuning and Optimization), Hadoop (Hive, Sqoop, etc.), Python, SQL, and Tableau. He is currently working as a Business Intelligence Engineer II at Amazon and has previously worked as a Data Scientist Intern at BMW Group Financial Services North America, LLC, and as a Data Engineer and Analyst at TCS. At BMW, Nishant implemented Random Forest, SVM, and Boosting Algorithms to predict customers' likelihood to pay off the delinquent amount. He also derived statistical inferences and identified significant features using regression model output, F-test, and T-test. Nishant created interactive visualizations in Tableau and Python to monitor KPIs affecting BMW's customers, recovery, and delinquency portfolios, which were influential in making further business decisions. He also utilized complex and efficient SQL queries to extract historic data for analytical reporting and model building. At TCS, Nishant cleaned, transformed, and loaded datasets in the range of 100 terabytes from various data sources into HDFS using Sqoop, SQL, and Hive scripts, facilitating data handling and analyzing capacity by 300%. He developed an ETL data pipeline framework using Python and Hive SQL, which reduced manual work hours by 80%. Nishant extracted and analyzed business's large customer data to generate analytical insights using Apache Spark, Hive, SQL, and Python Pandas, which aided clients further in making business decisions. He also tuned Spark jobs to achieve better performance, cleaner Spark code, and efficient memory management. Nishant holds a Master of Science in Information Technology and Management with a Data Science concentration from the University of Texas, and a Bachelor of Technology in Electronics and Communications Engineering from Jaipur Engineering College & Research Center. His tech