Mennat Allah Tarek
About Mennat Allah Tarek
Mennat Allah Tarek is a Supply Chain Manager at Procter & Gamble with a background in data science and computer engineering.
Current Title and Role
Mennat Allah Tarek currently serves as a Supply Chain Manager at Procter & Gamble. She has been in this role since 2020. Her responsibilities likely include overseeing the supply chain operations, ensuring efficient and effective procedures, and coordinating with suppliers and staff to improve productivity and customer satisfaction.
Previous Experience as Data Scientist
Before joining Procter & Gamble, Mennat Allah Tarek worked as a Data Scientist at Pharos Holding for Financial Investments. She held this position from 2017 to 2019, where she was based in Egypt. During her tenure, she would have applied her data analysis skills to contribute to the company's financial strategies and initiatives, utilizing data to help make informed decisions.
Education in Computer Engineering
Mennat Allah Tarek pursued her education at Ain Shams University. She completed a Bachelor of Science in Computer Engineering from 2013 to 2017. Following her undergraduate studies, she continued at Ain Shams University to achieve a Master of Science (MS) in Computer Engineering from 2018 to 2020. Her academic focus has provided her with a strong foundation in both software development and machine learning.
Master's Thesis on Liver Cancer Prediction
For her Master's thesis, Mennat Allah Tarek is working on a project focused on liver cancer prediction. This work involves integrating her expertise in machine learning with software development skills to develop predictive models that can be applied in healthcare, potentially improving early detection and treatment outcomes for liver cancer patients.
Integration of Machine Learning and Software Development
Mennat Allah Tarek aims to leverage her machine learning expertise combined with her software development skills to create models with practical applications across various fields. Her work reflects a holistic approach to technological integration, seeking to bridge the gap between theoretical machine learning models and their real-world applications.