Katarzyna Hewelt
About Katarzyna Hewelt
Katarzyna Hewelt is a Data Scientist with experience in data analysis and programming training. She has worked for various companies, including NEPCO, Refinitiv, and CodiLime, and holds degrees in Power Engineering and International Economics.
Work at CodiLime
Katarzyna Hewelt has been employed at CodiLime as a Data Scientist since 2022. In this role, she focuses on developing machine learning models and natural language processing (NLP) solutions for forecasting. She also builds chatbot and AI assistant solutions, enhancing user interaction and data accessibility. Additionally, she provides analytical support to the business, contributing to data-driven decision-making processes. Her work involves utilizing PySpark for preprocessing, cleaning, and transforming large-scale data sets, which is essential for predictive modeling.
Previous Experience
Prior to her current role, Katarzyna worked at several organizations. She served as a Data Analyst at Refinitiv, an LSEG business, from 2019 to 2021 in Gdynia, Poland. Before that, she held the position of Junior Data Scientist at rynekpierwotny.pl for 11 months in Warsaw. She also gained international experience as an International Operations Specialist at Digital Bank Company for six months in Beijing, China, and completed internships at the National Electric Power Company (NEPCO) in Jordan and Wison Engineering Ltd. in Beijing.
Education and Expertise
Katarzyna holds a Master's degree in International Economics from the University of Gdansk, which she completed from 2016 to 2018. She also earned a Bachelor of Engineering in Power Engineering from Gdańsk University of Technology between 2012 and 2016. Additionally, she studied Data Science at infoShare Academy in 2021. Her educational background is complemented by her studies in Chinese Language at the Beijing Institute of Technology from 2017 to 2018.
Technical Skills
Katarzyna Hewelt possesses a range of technical skills relevant to her role as a Data Scientist. She utilizes PySpark for data preprocessing, cleaning, and transformation, which is critical for handling large-scale datasets. Her expertise extends to developing machine learning models and NLP solutions for forecasting. She also engages in web scraping to extract data for analysis, thereby enhancing data availability for various projects. Furthermore, she has authored an article discussing the significance of machine learning metadata and its impact on content embeddings.