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Abdalla Abdelmoaty

Abdalla Abdelmoaty

Senior Data Scientist

Baker Hughes | Saudi Arabia

Technologies

Data virtuoso, playing the strings of information to create harmonious insights.

My Work

Take a look at my latest work.

course

AI Ethics

course

Developing LLM Applications with LangChain

course

Designing Agentic Systems with LangChain

course

AI Ethics

DataCamp Course Completion

Take a look at all the courses I’ve completed on DataCamp.

Courses

AI Ethics
Introduction to AI Agents
Understanding Cloud Computing
Supervised Learning with scikit-learn

My Work Experience

Where I've interned and worked during my career.

Baker Hughes | Mar 2025 - Present

Senior Data Scientist

N/A

Baker Hughes | Jan 2023 - Mar 2025

Lead Engineer - Mathematics & Data Science

N/A

Madfu limited | Nov 2022 - Jan 2023

Senior Data Scientist

First Data Scientist at madfu, my work focuses on how data is used to improve every aspect of performance.
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Mak Home Care | Oct 2021 - Sep 2022

Information Technology Manager

N/A

Mak Home Care | Oct 2020 - Oct 2021

Information Technology Consultant

N/A

Liberty Mutual Insurance | Nov 2019 - May 2020

Data Scientist

Team: Abdalla Abdelmoaty, Eric McNeilly, Cristiano Costa, and Marc Arias Project Title: Identifying anomalies in the Safeco AutoQuote Mainframe system Background: Liberty Mutual’s Information Technology sector is the backbone and engine that makes the insurance company runs. Liberty processes billions of transactions a month and, occasionally, some transactions individually take a longer than usual time to run. These transactions consume up valuable and expensive resources in the Mainframe system. Liberty Mutual asked us to help identify these transactions that take excessively long to execute and to better understand the attributes that cause these transactions to be labeled as anomalies. Research Question: Is it possible to identify anomaly transactions and understand what makes them unique? Scope: Liberty Mutual provided data from Safeco AutoQuote representing around 86 million tasks from 9 computational parameters aggregated by day and hour that occurred between 9am until 2pm between December 2019 to March 2020. Methods: To address this question, the team utilized a number of techniques including feature engineering, Dimensionality Reduction, Clustering and Ensemble Anomaly Models. Deliverables and Business Impact: The deliverable was a well-documented ensemble system implemented in Python and delivered in a PowerBI dashboard. This model will be used to help the Mainframe operations team to identify anomalies in a more efficient way, increasing productivity, saving time and bringing better insights.

Darling Consulting Group | Nov 2019 - May 2020

Data Scientist

Team: Abdalla Abdelmoaty, Eric McNeilly, Cristiano Costa, and Marc Arias Project Title: Predicting a Financial Institutions’ non-maturity balances using publicly available call report data Background: Non-maturity deposits have an important role since it is the main fund for banks and their balance is related to changes in the Federal fund rate. In this context, Darling asked us to predict non-maturity deposit balances for all bank branches in the US and understand how fluctuations in the FED fund rate could affect those predictions in the next twelve months. Research Question: Is it possible to predict the non-maturity Deposit balance of bank branches for the next twelve months given a flat interest rate? Scope: The analysis used publicly available FDIC call sheet reports and included financial institutions that were active between March 2015 and September 2019 and with total assets less than 10 billion dollars. Methods: To address this question, the team utilized three main techniques including feature engineering, Time series clustering, and Long Short Term Memory networks. Deliverables and Business Impact: The deliverable was a well-documented ensemble system implemented in Python and delivered in a PowerBI dashboard. This model will be used to market Darling Consulting Group’s Deposits360 analytics services and provide insight into the banking community, increasing potential clientele and enhancing existing customer experience.

My Education

Take a look at my formal education

Master's degree, Data Science and AnalyticsUniversity of New Hampshire | 2020
Bachelor's degree in Economics and Political ScienceUniversity of Cincinnati | 2017

About Me

Abdalla Abdelmoaty

I believe the most interesting problems are the ones that haven't been defined yet. My work is a constant pursuit of that undefined territory, a place where a multi-disciplinary mindset is the only toolkit that matters.

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