Education:
• AI Professional Program – Institute of Information Technology (April -December 2021) The Artificial Intelligence Professional (AI-Pro) program is a 9-month post-graduate professional training program in Machine Learning and Artificial Intelligence. The program provides a unique learning experience with a balance between theoretical foundations, practical tools, and applications. The program is developed in partnership with the French Graduate School of Computer Science and Advanced Technologies (EPITA).
• Petroleum engineering B.Sc. – Suez University (2009 – 2014) Graduated with Very Good grade and graduation project; a comprehensive study on Raml Oil Field, Western Desert, Egypt.
Work Experience:
• Machine Learning Mentor at Udacity (May 2021 – now) The Machine Learning DevOps Engineer Nanodegree program focuses on the software engineering fundamentals needed to successfully streamline the deployment of data and machine-learning models in a production-level environment.
I help students in achieving these goals through answering technical questions on the Knowledge platforms and reviewing students’ projects submissions on these topics: – Implementing production-ready Python code/processes for deploying ML models outside of cloud-based environments facilitated by tools such as AWS SageMaker, Azure ML, etc. – Engineering automated data workflows that perform continuous training (CT) and model validation within a CI/CD pipeline based on updated data versioning – Creating multi-step pipelines that automatically retrain and deploy models after data updates – Tracking model summary statistics and monitor model online performance over time to prevent model-degradation
• Petroleum Engineer and Analytics at Prime Rock Energy Capital (August 2018 – now) A member of the core investment team providing vital technical analysis and petroleum engineering expertise for the team’s contemplated acquisitions. Key responsibilities: – Perform comprehensive analyses of our targeted oil and gas reservoirs, including full geological review, reservoir engineering modelling, decline curve analysis, and operator analysis. – Collaborate, as a Product Owner, with a software consultant to build our in-house software that can facilitate our daily tasks. This includes translating the team’s workflows into requirements (i.e. features, specifications, etc.) and managing requirements based on the product vision and roadmap. Building algorithms to integrate our core engineering module with the Front-End modules – Design and evaluate integrated predictive and decision models in Python to be deployed in deals evaluation systems, or for subsurface insightful research. – Develop technical presentations to support investor relations and fundraising with different quantitative analytics findings. – Collaboration with other teams to provide technical support in committee, investor, and fundraising meetings.
Major Achievements: – Built a python model to predict the price ranges of each acre in the Permian, Stack and Scoop Basins. This is based on evaluating reserves and building Type Curves for each section in Oklahoma and Texas. This saved us time and money as we automated most of the tier 1 deals. – Built an analytical model for forecasting the future operator activity and predicting the underlying risk rate for operators in the US. This saved us money by avoiding bankrupt operators in critical times. – Built the core statistical analysis and engineering modules for our in-house software and aligned with the Software Consultant to get it up and running on the Cloud.
• Team Leader at Uber (Jan – Aug 2018) Leading, motivating, and managing a 15-member team including setting daily targets, driving performance, monitoring queue, and liaising with city teams to optimize operations and facilitate insights.
Courses:
• Certified Data Scientist Professional by DataCamp (Sep 2021) I’ve got certified from DataCamp after multiple timed assessments, case study and a programming challenge. The certificate can be found here as well as the certification process.
• Advanced Data Analysis Nanodegree (Udacity) (Oct 2020) This program consists of mainly three parts: Data Analysis in Python and Pandas, Practical Statistics and A/B testing, and finally Data Visualization. Each part was followed by a practical project.
• Scientific Computing and Python for Data Science (June 2019) A comprehensive introduction to scientific computing, Python, and the related tools data scientists use to succeed delivered by The Data Incubator and WQU.
• Introduction to Computer Science and Programming Using Python (Mar 2016) MIT’s introduction into computational thinking and programming in Python, presenting many wonderful algorithms and scientific computing challenges.
• Python Specialization (Jan 2016) A four-course specialization about how to develop programs to gather, clean, analyze, and visualize data followed by a capstone project.