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Senior Machine Learning Scientist
I started my career into computer networks and information security. I worked in that field for 9 year, and I was leading a team of 5 at some point. But then in 2012, I felt the need to change my career. I was fascinated by machine learning already, and thus, decided to do my MSc in Machine Learning. After finishing my studies in 2013, I started my new career as a data scientist. I worked on different problems from natural language processing, to price estimation and recommendation engine. I have also published books on data visualization using D3.js and machine learning using Scikit-Learn and Python. I do see my skillsets as a t-shaped one. Although, my main interest is machine learning, I do build side projects using back-end and front-end web frameworks. I like to read about product management, behavioral economics and blockchain. I believe it is important to keep my eyes open to new things, in the end, you may need these intersections with multiple fields one day in your job.
University of East Anglia
Master of Science (MSc), Knowledge Discovery and Data Mining
January 1, 2012 – January 1, 2013
Cairo University
BSc., Electronics & Communications
January 1, 1998 – January 1, 2002
Dar El Tarbiya
General Secondary Education
January 1, 1994 – January 1, 1997
Adyen
Senior Machine Learning Scientist
January 1, 2022 – Present
Amsterdam, North Holland, Netherlands · On-site
TicketSwap
Lead Machine Learning Engineer
September 1, 2018 – December 1, 2021
The Randstad, Netherlands
Catawiki
Machine Learning Data Scientist
May 1, 2016 – September 1, 2018
The Randstad, Netherlands
bidx
Data Scientist
December 1, 2014 – May 1, 2016
The Randstad, Netherlands
Meedan
Computational Linguist
March 1, 2014 – December 1, 2014
Egypt
The Times
Data Journalist (Intern)
September 1, 2013 – October 1, 2013
London Area, United Kingdom
Salec "a BARQ Group Company"
Lead Network Architect
January 1, 2011 – September 1, 2012
Global Voices Online
Author (Volunteer)
October 1, 2007 – January 1, 2013
Salec "a BARQ Group Company"
Network Security Architect
April 1, 2007 – January 1, 2011
Salec "a BARQ Group Company"
Systems Engineer (Team Leader)
June 1, 2003 – April 1, 2007
Technowireless 'a SAB Holding Company'
Software Developer
December 1, 2002 – May 1, 2003
Mwazna
March 1, 2015 – Present
An attempt to explain and visualise government budget and make it available to everyone
Do you speak London?
March 1, 2014 – Present
Command line tool for natural language identification, also known as langID. Currently supporting 4 languages only, English, Spanish, Portuguese and Arabic.
Data Journalism Training
December 1, 2013 – Present
Training Welad El-Balad on the basics of Data Journalism and Infographics. The training included Data Analysis using Spreadsheets, Data Visualisation and Design Principles, Data Scraping, Charting and Mapping tools, Video Annotation and Preparing Visuals for Print. We used tools such as TileMill, InkScape, Mozilla Popcorn, Datawrapper, RAW by Density Design and Tabula
OpenMENA
December 1, 2013 – Present
The Open Knowledge Foundation (OKFN) is a non-profit organisation founded in 2004 and dedicated to promoting open data and open content in all their forms – including government data, publicly funded research and public domain cultural content. Open MENA is a new project bootstrapped by OKFN’s community interested to bring the values and ethics of openness in the Arab World, from North Africa to the Gulf going through the Levant. http://mena.okfn.org/
URL-based Web Pages Classification
August 1, 2013 – Present
In my dissertation I proposed a new approach to classify web-pages based on their URLs only. I tried to tackle the problem where solutions improves the classifiers accuracy at the expense of their scalability.
Predict whether a new customer will buy a caravan insurance policy.
March 1, 2013 – Present
This dataset was used for the Coil 2000 data mining competition. It contains customer data for an insurance company. The feature of interest is whether or not a customer buys a caravan insurance. Per possible customer, 86 attributes are given: 43 socio-demographic variables derived via the customer's ZIP area code, and 43 variables about ownership of other insurance policies. The data mining task is to predict whether someone will buy a caravan insurance policy. You should first do some exploratory data analysis. Visualising the data should give you some insight into certain particularities of this dataset. Then prepare the data for data mining. It will be important to select the right features, and to construct new features from existing ones, as is described in the paper of the prediction competition winner. Try out at least 2 different data mining algorithms, and compare the use of mere feature selection with intelligent feature construction. As an extra, you could try to do the second task laid out in the Coil competition: to derive information about the profile of a typical caravan insurance buyer. Artificial Neural Networks (Weka Multilayer Perceptron) and Decision Trees (Weka J48) were used here, in addition to Meta Cost, SMOTE Class Balancing and Filters/Wrappers Feature Selection algorithms. Orange Open Source Data Visualization and Analysis package was also use with custom plugins for Data Cleaning and Exploratory Data Analysis
IRLib
December 1, 2012 – Present
A Python Information Retrieval and Machine Learning library
Deceptive Opinion Spam Classification System
November 1, 2012 – Present
With a continuous incorporation of IT in all settings of life, websites had been developed that focused exclusively on providing user reviews products and services. In a very competitive world, however, it was seen that such websites would often be inundated with fake reviews --- reviews from people who are not actual users of a product/service. Such reviews either undeservedly praise the respective product/service, or degrade them, making sake reviews a cause of concern for the product manufacturer/service provider. This project had the goal of developing techniques from Information Retrieval and Machine Learning in order to classify such fake/spam reviews in the context of Hotel reviewing. A part of the assignment was to write the classifiers ourselves, without using any off-the-shelf software packages. The 3 classifiers were built using Python programming language. Classifiers used: Naive Bayes, kNN and Rocchio Results Obtained: 88.5% Accuracy using Naive Bayes Classifier Dataset reference: M. Ott, Y. Choi, C. Cardie, and J.T. Hancock (2011). Finding Deceptive Opinion Spam by Any Stretch of the Imagination. Proc. 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies.
Cultural Fit Analysis
The candidate's project history, including 'OpenMENA' and 'Mwazna', demonstrates an interest in open data, social impact, and making complex information accessible, which could indicate a strong cultural fit for organizations valuing transparency and public good. The diverse range of projects and roles, from network architecture to data journalism and machine learning, suggests adaptability and a broad intellectual curiosity. However, the early career in network architecture is a significant pivot, and while it shows versatility, it also means less direct, continuous experience in the target domain compared to someone who started directly in ML/NLP.
Soft Skills & Operational Fit
The candidate's experience as a Lead Machine Learning Engineer at TicketSwap indicates strong leadership, team goal setting, and mentoring abilities. Their work at Catawiki and Adyen highlights a focus on business impact and iterative solution building, suggesting a pragmatic and outcome-oriented approach. The 'Data Journalism Training' project also shows an aptitude for communication and teaching complex technical concepts.