Daniel Arenson
About Daniel Arenson
Daniel Arenson is a Data Engineer at Hopper with a background in data analysis and operations, having previously worked at Sagence, Inc., ACCUMEN Inc., UC San Diego Health, and NYU Langone Medical Center.
Current Role: Data Engineer at Hopper
Daniel Arenson currently works as a Data Engineer at Hopper. In this role, he applies his expertise in data science and engineering to develop and maintain data systems that enhance Hopper’s operations. His responsibilities likely include data pipeline construction, data storage solutions, and the development of algorithms for data processing. His work contributes to the company's goals of improving travel booking experiences through data-driven insights.
Previous Experience at Sagence, Inc.
Before joining Hopper, Daniel Arenson served as an Associate at Sagence, Inc. for one year from 2021 to 2022 in the Greater Chicago Area. During his tenure, he would have been involved in providing data-driven solutions tailored to clients’ needs. His role focused on leveraging data analytics to drive business insights and outcomes.
Senior Data Analyst at ACCUMEN Inc.
From 2019 to 2020, Daniel Arenson held the position of Senior Data Analyst at ACCUMEN Inc. Here, he specialized in analyzing and interpreting complex data sets to aid healthcare professionals in making informed decisions. His work involved predictive analytics and exploratory data analysis, ensuring that data-driven strategies were effectively implemented.
Educational Background in Data Science and Biology
Daniel Arenson has a solid educational foundation in both data science and biological sciences. He earned a Master of Science (MS) in Data Science from Northwestern University, where he deepened his skills and knowledge in this field from 2019 to 2021. Prior to that, he obtained a Bachelor of Arts (B.A.) in Biology/Biological Sciences, General from Binghamton University, which he attended from 2008 to 2012.
Specializations in Data Science
Daniel Arenson specializes in several key areas within data science including Natural Language Processing, Machine Learning, Search Engines, Recommendation Systems, Exploratory Data Analysis, and Predictive Analytics. These specializations allow him to tackle complex data challenges and develop sophisticated models and systems to derive actionable insights from data.