Piotr Maciejewski
About Piotr Maciejewski
Piotr Maciejewski is a Senior Machine Learning Scientist at Turnitin, where he has worked since 2023. He has a background in data science and financial modeling, with previous roles at companies such as smartpatient and Link4.
Work at Turnitin
Piotr Maciejewski has been employed at Turnitin as a Senior Machine Learning Scientist since 2023. His role is remote, allowing him to contribute to the organization from various locations. At Turnitin, he focuses on developing machine learning solutions that enhance the company's capabilities in academic integrity and writing assessment.
Previous Experience in Data Science
Before joining Turnitin, Piotr worked as a Data Scientist at Smartpatient from 2022 to 2023 for nine months in Warsaw, Poland. He also held a Data Scientist position at Link4 from 2020 to 2022 for two years, where he contributed to data-driven decision-making processes. Additionally, he served as a Data Analyst at Samba TV in 2019 for eleven months, further solidifying his expertise in data analysis.
Consulting and Internship Background
Piotr's career includes experience as a Consultant in valuation and financial modeling at TS Partners from 2016 to 2018 for two years. He also completed internships at Crido Taxand in 2015 for two months in European Advisory Services and at ING Bank Śląski in the Commercial Real Estate Finance Department for one month. These roles provided him with a strong foundation in financial analysis and consulting.
Education and Expertise
Piotr Maciejewski studied at SGH Warsaw School of Economics, where he earned a Licencjat (Lic.) in Finance and Accounting from 2011 to 2014 and a Magister (Mgr) in the same field from 2014 to 2016. He also completed a Data Science bootcamp at Sages Sp z o.o. in 2018. His educational background equips him with a robust understanding of finance, data science, and machine learning.
Technical Projects and Contributions
Throughout his career, Piotr has executed several technical projects. He developed a Proof of Concept for a textual data anonymization pipeline using FastAPI and Docker, achieving over 90% recall. He also created a solution architecture for a data gathering pipeline utilizing AWS infrastructure and deployed it using Terraform. Additionally, he is involved in developing automatic monitoring systems for comparing large language models with AI writing detectors.