Menglu Huang
About Menglu Huang
Menglu Huang is a Data Scientist with a Master's degree in Statistics from Carnegie Mellon University and a Bachelor's degree in Economics from Boston College. She has experience in various analytical roles, including her current position at E Source, where she specializes in people analytics and risk analysis.
Current Position at E Source
Menglu Huang currently serves as a Data Scientist at E Source, a position she has held since 2021. In this role, she focuses on various data science initiatives, including people analytics segmentation and risk analysis. She actively monitors the entire data science life cycle, utilizing weekly sprints and code reviews to ensure project efficiency and quality. Her expertise in energy usage load profiling and propensity enrollment models contributes to the organization's data-driven decision-making processes.
Education and Academic Background
Menglu Huang completed her Bachelor's degree in Economics at Boston College from 2016 to 2019. She furthered her education by obtaining a Master's degree in Statistics from Carnegie Mellon University, where she studied from 2019 to 2020. Prior to her college education, she graduated from Lexington Christian Academy and Monsignor Donovan High School, achieving her high school diplomas in 2012 and 2016, respectively.
Previous Work Experience
Menglu Huang has held several positions prior to her current role at E Source. She worked as a Statistical Consultant at the Open Learning Initiative for four months in 2020. Additionally, she served as a Research Assistant at the University of Pittsburgh for three months in 2016. Her experience also includes a role as a Policy Analyst at APPRISE from 2020 to 2021 and as an Analyst at Yelp for Restaurants in 2019. Furthermore, she was a Teaching Assistant at Carnegie Mellon University from 2019 to 2020.
Technical Skills and Specializations
Menglu Huang possesses a strong technical skill set in data science, particularly in high-dimensional feature selections and hyperparameter tuning. She has spearheaded projects utilizing natural language processing to analyze survey and demographic data, identifying key factors that influence customer satisfaction and retention. Additionally, she built a time series analysis pipeline that integrated new business projects across multiple service territory levels.