Dan Roth
About Dan Roth
Dan Roth is a Data Analytics Manager at The New York Times, specializing in subscriber reporting and analytics. He has a background in audio engineering and data science, with experience in machine learning and natural language processing.
Work at The New York Times
Dan Roth currently serves as the Data Analytics Manager for Subscriber Reporting and Analytics at The New York Times. He has held this position since 2022, contributing to the organization for two years in New York, New York. Prior to this role, he worked as a Senior Data Analyst in the same department from 2021 to 2022 and as a Data Analyst from 2020 to 2021. His work focuses on analyzing subscriber data to inform strategic decisions.
Education and Expertise
Dan Roth earned a Bachelor of Arts in East Asian Audio Engineering from New York University, completing his studies from 2010 to 2014. He later pursued a Master of Science in Audio Engineering at the University of Derby, studying from 2018 to 2019. Additionally, he graduated from the Metis Data Science Bootcamp in New York City, where he focused on data science for 11 months in 2019. His expertise includes machine learning for audio, natural language processing (NLP), and automatic speech recognition systems.
Background
Dan Roth has a diverse professional background that includes experience in data analytics and audio engineering. He worked as a Graduate Student at the University of Derby from 2018 to 2019. Prior to his current role, he was a Contributing Writer at Towards Data Science from 2019 to 2021. He also served as a Data Science Student at Metis for three months in 2019. His early education includes a High School Diploma from Ward Melville High School, which he obtained from 2007 to 2010.
Skills and Specializations
Dan Roth possesses a range of technical skills relevant to data analytics and audio engineering. His expertise includes natural language processing techniques such as topic modeling, recommender systems, and conversational AI. He is also skilled in web scraping and has a background in the recording industry, where he produced sessions for voice-over artists, film post-production teams, and musicians. His experience in machine learning specifically relates to audio applications, including music and speech.