Dan Arundel
About Dan Arundel
Dan Arundel is a Senior Data Scientist currently working at OXIO in Denver, Colorado. He has extensive experience in data science and analytics, having held various roles at Guild and DISH Network, and possesses advanced skills in machine learning and cloud technologies.
Current Role at OXIO
Dan Arundel serves as a Senior Data Scientist at OXIO, a position he has held since 2023. He is based in Denver, Colorado, where he applies his expertise in data science to develop and implement advanced analytical solutions. His role involves deploying machine learning models and managing complex data workflows, contributing to the company's data-driven initiatives.
Previous Experience at Guild
Prior to his current role, Dan worked at Guild in various capacities. He served as a Data Scientist from 2021 to 2022, then advanced to Senior Data Scientist from 2022 to 2023. His tenure at Guild included a brief period as Lead Data Scientist in 2023. Throughout his time there, he focused on leveraging data analytics to drive business decisions.
Professional Background in Data Science
Dan's professional journey includes significant experience in data science roles. He worked at DISH Network as a Data Scientist from 2018 to 2021, where he focused on customer experience analytics. His earlier experience includes positions at Deloitte, where he held various auditing roles from 2014 to 2018, providing him with a strong foundation in data analysis and financial insights.
Educational Background
Dan holds a Master's degree in Accounting from The Ohio State University Fisher College of Business, which he completed in 2013. He also earned a Master of Science in Business Analytics from the W. P. Carey School of Business at Arizona State University in 2019. His undergraduate studies culminated in a Bachelor of Science in Accounting from Bowling Green State University in 2012.
Technical Skills and Expertise
Dan possesses expertise in deploying machine learning models using AWS services, including Lambda and S3. He utilizes advanced libraries such as HuggingFace and TensorFlow for developing production-ready solutions. Additionally, he leverages orchestration tools like Airflow and Step Functions for managing data pipelines and employs containerization technologies like Docker and Kubernetes to enhance machine learning workflows.