Ahmed Shaher, Developer in Cairo, Cairo Governorate, Egypt
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Ahmed Shaher

Verified Expert  in Engineering

Data Scientist and Machine Learning Developer

Location
Cairo, Cairo Governorate, Egypt
Toptal Member Since
June 8, 2021

Ahmed is a senior data scientist with experience in innovative data science projects, including four years at Microsoft where he has worked on projects in the fintech, eCommerce, media, and oil and gas sectors. Ahmed has collaborated with customers' engineering teams and built complete pipelines, including data ingestion, machine learning models, and deployment to production. His industry experience is backed by a master's in computer science and machine learning from a top US business school.

Portfolio

Microsoft
Data Science, Machine Learning, Azure Machine Learning, Deep Learning...
Elves
Python, Data Analysis, Churn Analysis, Machine Learning, Classification...
Benchmark Labs Middle East
JavaScript, GPT, Generative Pre-trained Transformers (GPT)...

Experience

Availability

Part-time

Preferred Environment

Python, Data Science

The most amazing...

...thing I've created is a pipeline, including a ML model that increased access to short-term loans for a fintech by 24% through proper risk assessment.

Work Experience

Senior Data Scientist

2018 - PRESENT
Microsoft
  • Worked in an innovation team in a Sales department. The team connects research, advanced technologies, and Microsoft strategic customers.
  • Co-innovated with Microsoft strategic customers by creating data platforms involving data ingestion, storage, and insights. This was done after engaging with the customer in exploratory data analysis workshops.
  • Created machine learning models to solve advanced problems, such as a credit risk assessment, a meta-learning video classification, and a recommendation engine over GraphDB based on customer scenarios.
Technologies: Data Science, Machine Learning, Azure Machine Learning, Deep Learning, Azure Data Factory, Python, JavaScript, Financial Data, EDA, Recommendation Systems, GraphDB, Social Networks, Social Network Analysis, Credit Risk, Classification, Deep Reinforcement Learning, eCommerce, Clustering, Azure, Azure Functions, Computer Engineering, Data Analysis, Machine Learning Operations (MLOps), GPT, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Data Visualization, Algorithms, SQL, Reinforcement Learning, Software Development, Design Patterns, Software Deployment, Churn Analysis, Azure SQL Databases, NoSQL, Risk Models, Consumer Loans, Plotly, Modeling, Statistical Modeling, Data Modeling, Data Engineering, PyTorch, Predictive Analytics, Artificial Intelligence (AI)

Data Scientist

2018 - 2018
Elves
  • Developed custom named entity recognition for airports and airlines to annotate travel queries and automate the flight booking flow.
  • Built a reporting system for exploratory and predictive analytics to find trends and anomalies in the data. The system also reported on churning users and identified common scenarios to re-engage with a segment of churning users.
  • Trained an LSTM spam classifier to identify users who have malicious behavior.
Technologies: Python, Data Analysis, Churn Analysis, Machine Learning, Classification, Clustering, Computer Engineering, Google Data Studio, Named-entity Recognition (NER), EDA, LSTM, Deep Learning, Algorithms, Reinforcement Learning, Software Development, Data Science, Software Deployment, SQL, NoSQL, Plotly, Modeling, Data Modeling, Predictive Analytics, Artificial Intelligence (AI)

Data Scientist

2017 - 2018
Benchmark Labs Middle East
  • Developed a book consultant chatbot (sipof.ink) that won first place in the Facebook Middle East and Africa Bots for Messenger Challenge in Productivity and Utility.
  • Trained a Doc2Vec model on book data to find similarities and similar books to recommend.
  • Trained an end-to-end memory network (MemN2N) for book recommendations and book question answering.
Technologies: JavaScript, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), GPT, Algorithms, Python, Machine Learning, Software Development, Computer Engineering, Data Science, Software Deployment, Classification, Word2Vec, SQL, NoSQL, Modeling, Data Modeling, Predictive Analytics, Artificial Intelligence (AI)

Junior Data Scientist

2016 - 2017
Cognitive
  • Created a product linkage pipeline consisting of three stages—indexing, matching, and classification—to enable the creation of a master product from different eCommerce websites.
  • Achieved 77% accuracy on products in both Arabic and English across three different eCommerce websites.
  • Enabled users to compare products on different eCommerce websites, using this three-stage pipeline to match products with as little data as title and price range and create the master product page.
Technologies: Python, eCommerce, Machine Learning, Clustering, Classification, Text Classification, Word2Vec, Algorithms, Software Development, Computer Engineering, Data Science, Software Deployment, Modeling, Data Modeling, Predictive Analytics, Artificial Intelligence (AI)

Credit Risk Assessment and Limit Prediction for Fintech

An MLOps pipeline I created, including a machine learning model that used small and medium business transactions, assessed their credit risk classifications, and determined the proper limits for payment extensions. This increased access to short-term loans for the fintech by 24% and decreased the default rate by 5%.

Textual Emotion Recognition Using Ensemble Classifier

Implemented an ensemble classifier system to classify whether the emotion in an input text is anger, disgust, fear, sadness, or joy. The system is based on three learners: two statistical learners and a knowledge-base learner, ensembled in a majority voting approach. The ensemble was able to classify emotions from tweets with a 95% F1 score.

Dashboard for User Purchasing and Churn Analyses

Developed a reporting system for exploratory and predictive analytics to find trends and anomalies in the data. The system also reported churning users and identified common scenarios to re-engage with a segment of churning users based on their previous purchasing patterns, accounting for seasonality. The dashboard was created in Python and visualized on Google Data Studio. Using this project, the marketing team was able to promptly engage with users and decrease the purchase churning percentage by 24%.

Languages

Python, SQL, JavaScript

Libraries/APIs

SciPy, LSTM, PyTorch, Natural Language Toolkit (NLTK)

Tools

Azure Machine Learning, Plotly, Named-entity Recognition (NER)

Paradigms

Data Science, Design Patterns

Platforms

Azure, Azure Functions

Other

Machine Learning, Reinforcement Learning, Deep Learning, Software Development, Computer Engineering, Software Deployment, Data Analysis, Natural Language Processing (NLP), Clustering, Classification, Text Classification, Ensemble Methods, EDA, Recommendation Systems, Credit Risk, Algorithms, Modeling, Data Modeling, Predictive Analytics, Artificial Intelligence (AI), GPT, Generative Pre-trained Transformers (GPT), Deep Reinforcement Learning, Machine Learning Operations (MLOps), Azure Data Factory, eCommerce, Word2Vec, Financial Data, GraphDB, Social Networks, Social Network Analysis, Google Data Studio, Data Visualization, Time Series Analysis, Risk Models, Statistical Modeling, Data Engineering, Churn Analysis, Trend Analysis, Consumer Loans, Trading, Trading Applications, OCR

Storage

Azure SQL Databases, NoSQL

2019 - 2021

Master's Degree in Computer Science

Georgia Institute of Technology - Atlanta, Georgia, USA

2011 - 2016

Bachelor's Degree in Computer Engineering

Ain Shams University - Cairo, Egypt

JANUARY 2021 - JANUARY 2023

Microsoft Azure Developer Associate

Microsoft

MARCH 2020 - MARCH 2022

Microsoft Certified: Azure Data Scientist Associate

Microsoft

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