Mohammed E.

Mohammed E. has a strong background in machine learning engineering and data engineering. Mohammed most recently held the position of Data Engineer at Aider, starting in September 2022. Prior to that, they worked as a Machine Learning Engineer at AMNESIA Media from July 2021 to August 2022. Mohammed also worked at SUEZ Smart Solutions as a Machine Learning Engineer from January 2021 to June 2021. Mohammed gained teaching experience as a Machine Learning Teaching Assistant at Auckland University of Technology - School of Engineering, Computer & Mathematical Sciences from January 2020 to December 2020. Earlier in their career, Mohammed worked as a Machine Learning Engineer at Absolute ML from August 2019 to December 2020, and as an Analytics Project Manager at Ipsos from July 2017 to August 2019.

Mohammed E. completed their Master of Analytics degree at Auckland University of Technology from 2019 to 2021. Before that, they obtained a Bachelor's degree in Management Information Systems from SADAT ACADEMY for MANAGEMENT SCIENCES, which they attended from 2000 to 2004.

In addition to their formal education, Mohammed also holds various certifications. These include certifications in supervised machine learning from DeepLearning.AI, certified cloud practitioner and certified machine learning engineer from Amazon Web Services (AWS), AI for everyone from DeepLearning.AI, cloud machine learning engineering and MLOps from Duke University, transfer learning for NLP with TensorFlow Hub from Coursera, and deep learning essentials from IBM. Mohammed has also completed training in version control with Git from Udacity, machine learning using SAS from SAS, and customer experience management professional from Medallia.

Furthermore, Mohammed acquired certifications as a certified Publons Academy peer reviewer from Publons and a certified ACS peer reviewer from the ACS Reviewer Lab - American Chemical Society. Lastly, they obtained a certification in What is the Metaverse? from Meta.

It is important to note that no strong assumptions were made outside of the provided information.