Aditya Jhanwar
About Aditya Jhanwar
Aditya Jhanwar is a Data Analyst at Flexport in San Francisco, specializing in statistical methodologies and machine learning.
Current Role at Flexport
Aditya Jhanwar works as a Data Analyst at Flexport in San Francisco, California, United States. He is part of the Compliance and Legal teams, where he utilizes his expertise in statistical methodologies and machine learning to address various business-related questions. His proficiency in multiple programming languages and analytical skills support his role in analyzing data to ensure compliance and legal standards are met.
Previous Experience at Marvell Technology
Before joining Flexport, Aditya Jhanwar worked as a Data Scientist at Marvell Technology in Santa Clara, California, United States, from 2020 to 2021. During his one year at Marvell Technology, he focused on utilizing statistical analysis and machine learning to support the company's technological advancements. His role encompassed predictive modeling and analytics to drive data-driven decision-making processes.
Educational Background at UC Berkeley
Aditya Jhanwar earned his Bachelor's degree in Statistics from the University of California, Berkeley, where he studied from 2017 to 2019. His academic focus included areas such as statistical methodologies and machine learning. He gained comprehensive skills in statistical analysis, particularly descriptive and inferential statistics, and developed a concentration in machine learning as part of his BA in Statistics.
Educational Foundation at Chabot College
Aditya Jhanwar began his higher education at Chabot College, studying Computer Science from 2015 to 2017. He achieved an Associate's degree, which laid the foundation for his subsequent studies in statistics and machine learning. His time at Chabot College provided him with essential knowledge in computer science principles and programming, which he later built upon in his professional and academic pursuits.
Technical Skills and Specializations
Aditya Jhanwar specializes in statistical methodologies and machine learning, with a particular focus on predictive modeling and analytics. He is proficient in multiple programming languages, including SQL, R (tidyverse, prophet, glmnet, Shiny), Python (NumPy, scikit-learn), and C++. His expertise also covers time series analysis and product analytics, making him well-equipped to handle complex data analysis tasks in both scientific and business contexts.