Ariel Karp
About Ariel Karp
Ariel Karp is a Data Scientist with a Master's degree in Data Science from Ben-Gurion University of the Negev. He has experience in various roles, including QA Officer at Alere Inc. and Data Analyst at G-STAT, and currently works at Anaplan, focusing on innovation projects involving natural language processing and optimization.
Work at Anaplan
Ariel Karp has been employed at Anaplan as a Data Scientist since 2020. In this role, Karp focuses on utilizing advanced data science techniques to enhance business operations. The position involves working with large datasets and applying statistical models to derive insights that inform decision-making processes.
Education and Expertise
Ariel Karp holds a Master's degree in Data Science from Ben-Gurion University of the Negev, completed between 2018 and 2021. Karp also earned a Bachelor's degree in Industrial Engineering and Management from the same institution, graduating in 2018. Karp has expertise in building and evaluating statistical models using Python and SQL, and is skilled in big data architecture, including technologies such as MongoDB, AWS, Postgres, and Redshift.
Background in Data Science and Analytics
Prior to joining Anaplan, Karp worked at G-STAT as a Data Analyst and BI Developer from 2018 to 2020. Karp also served as a Teaching Assistant at Ben-Gurion University from 2018 to 2021 and as a Mathematics Tutor from 2016 to 2018. Earlier experience includes a role as a QA Officer at Alere Inc. from 2012 to 2014 and as a System Implementor in the Israel Defence Forces from 2011 to 2012.
Achievements in Predictive Modeling
As part of Karp's Master's thesis, a predictive statistical model was developed for public transportation timeseries data. This project highlights Karp's ability to apply theoretical knowledge to practical challenges in data science, particularly in the context of transportation analytics.
Focus on Innovation and Optimization
Ariel Karp's work emphasizes renewing legacy processes and algorithms, alongside engaging in innovation projects that involve natural language processing (NLP) and optimization techniques. This focus reflects a commitment to advancing data science applications within various operational frameworks.