Nicholas Stang
About Nicholas Stang
Nicholas Stang is a Data Science Manager with a strong background in data science and analytics, having previously held positions at companies such as Intuit and Ameriprise Financial Services. He possesses technical skills in various programming languages and has a solid educational foundation in Mathematics and Applied Economics.
Current Role at Foodsby
Nicholas Stang serves as the Data Science Manager at Foodsby since 2020. In this role, he focuses on developing data-driven solutions that enhance productivity and improve business processes. His expertise in machine learning and big data technologies allows him to deliver personalized services, contributing to the company's operational efficiency.
Previous Experience at Foodsby
Before his current position, Nicholas worked at Foodsby as a Senior Staff Data Scientist for 9 months in 2019. During this time, he developed data solutions that significantly improved business processes. His contributions laid the groundwork for his subsequent promotion to Data Science Manager.
Professional Background
Nicholas has a diverse professional background in data science and analytics. He worked at Intuit as a Staff Data Scientist from 2016 to 2019 and at Ameriprise Financial Services, Inc. as an Econometrician II from 2013 to 2016. His earlier roles include being an Alpine Ski Coach at Vail Resorts and a Business Analyst at Digital River.
Education and Expertise
Nicholas holds a Bachelor of Arts in Mathematics from St. Olaf College and a Master of Science in Applied Economics from the University of Minnesota-Twin Cities. He possesses technical proficiency in various programming languages and tools, including Spark (Scala), Python (NumPy, Pandas, sklearn), R, and SQL, which he leverages in his data science work.
Internship Experience
In 2011, Nicholas completed a Marketing and Sales Internship at State Farm Insurance for 4 months. This early experience provided him with foundational skills in marketing and analytics, complementing his later roles in data science and econometrics.