Gauri Powale
About Gauri Powale
Gauri Powale is a Senior Data Scientist currently working at D2iQ in the Los Angeles Metropolitan Area. With a strong background in data science and software engineering, she has held various positions at D2iQ and has contributed to research at multiple prestigious institutions.
Current Role as Senior Data Scientist
Gauri Powale currently holds the position of Senior Data Scientist at D2iQ, having started in this role in 2021. She operates from the Los Angeles Metropolitan Area and has accumulated three years of experience in this capacity. In her current role, she focuses on advanced data analysis and machine learning techniques to drive business insights and enhance decision-making processes.
Previous Experience at D2iQ
Before her current position, Gauri Powale worked at D2iQ in two different roles. She served as a Data Scientist from 2019 to 2021 for two years and as a Software Engineer II from 2018 to 2019 for one year. During her tenure, she contributed to various projects, including the implementation of an unsupervised anomaly detection algorithm and the development of a random forest classification model to predict customer churn.
Educational Background
Gauri Powale has a strong educational background in computer science and mathematics. She earned a Master's degree in Computer Science from Georgia Institute of Technology. Additionally, she completed her Bachelor's degree in Applied Mathematics and Physics at the University of California, Berkeley.
Research Experience
Gauri has gained valuable research experience through various internships and assistantships. She worked as an Undergraduate Research Assistant at the U.S. Naval Research Laboratory from 2011 to 2013 for two years, and at the University of California, Berkeley from 2014 to 2016 for two years. She also spent two months as an Undergraduate Research Assistant at The University of Edinburgh in 2015.
Technical Skills and Contributions
Gauri Powale has demonstrated her technical skills through the development of various analytical models. She enhanced revenue forecasting accuracy using an ARIMA financial model and implemented an unsupervised anomaly detection algorithm to improve real-time telemetry logs analysis. Her work reflects a strong foundation in data science and machine learning methodologies.