Senior Data Scientist
2018 - PRESENTMicrosoft- 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), Natural Language Processing (NLP), 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 - 2018Elves- 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 - 2018Benchmark 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, Natural Language Processing (NLP), 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 - 2017Cognitive- 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, Product Linkage, Machine Learning, Clustering, Classification, Text Classification, Word2Vec, Algorithms, Software Development, Computer Engineering, Data Science, Software Deployment, Modeling, Data Modeling, Predictive Analytics, Artificial Intelligence (AI)
Ahmed Shaher
Data Scientist and Machine Learning Developer in Cairo, Cairo Governorate, Egypt
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.
Ahmed is now available for hire
Portfolio
- MicrosoftData Science, Machine Learning, Azure Machine Learning, Deep Learning...
- ElvesPython, Data Analysis, Churn Analysis, Machine Learning, Classification...
- Benchmark Labs Middle EastJavaScript, Natural Language Processing (NLP), Algorithms, Python...
Experience
- Python 8 years
- Data Science 5 years
- Machine Learning 5 years
- Azure Machine Learning 3 years
- Deep Learning 3 years
- Reinforcement Learning 3 years
- Azure 3 years
- Machine Learning Operations (MLOps) 3 years
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.
Employment
Experience
- 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%.
Skills
Languages
Python, SQL, JavaScriptLibraries/APIs
SciPy, LSTM, PyTorch, NLTKTools
Azure Machine Learning, Plotly, Named-entity Recognition (NER)Paradigms
Data Science, Design PatternsPlatforms
Azure, Azure FunctionsOther
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), 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, Product Linkage, Trend Analysis, Consumer Loans, Trading, Trading Applications, OCRStorage
Azure SQL Databases, NoSQL
Education
- Master's Degree in Computer Science2019 - 2021Georgia Institute of Technology - Atlanta, Georgia, USA
- Bachelor's Degree in Computer Engineering2011 - 2016Ain Shams University - Cairo, Egypt
Certifications
- Microsoft Azure Developer AssociateJANUARY 2021 - JANUARY 2023Microsoft
- Microsoft Certified: Azure Data Scientist AssociateMARCH 2020 - MARCH 2022Microsoft
