FinTech · Credit Risk · Fraud Detection · Intelligent Systems · Applied Research · Marketing Engineering
Building intelligent systems for banking, risk, fraud detection, and business growth. I work across both data science and AI engineering, from model to production.
I'm a Bahraini Senior Data Scientist Lead and Certified AI Engineer. In simple terms, I work on both sides of intelligence: building the brain and bringing it to life.
As a Data Scientist and Applied Researcher, I conduct research in the AI domain to define and develop new methodologies, where I wrangle, clean, structure, and analyze data then build, train, test, and improve AI and machine learning models across computer vision, NLP, deep learning, GenAI, AI agents, prediction, forecasting, and more.
As an AI Engineer, I take those models and turn them into real systems people can actually use: deploying them, connecting APIs, automating workflows, monitoring performance, and making sure they work reliably in production.
I enjoy combining Marketing with Data Science known as Marketing Engineering. Marketing Engineering is the fusion of marketing, data science, and AI. It enables organizations to move beyond intuition and make decisions based on prediction, customer intelligence, and quantitative evidence where I build those models.
"A Data Scientist builds the brain. An AI Engineer gives it a body and connects it to the world. That is where I operate, between data, mathematics, machine learning, and real-world AI systems."
Statistical analysis, machine learning, model development, deep learning, NLP, computer vision, forecasting, and GenAI.
Model deployment, API integration, workflow automation, production monitoring, and building reliable AI systems.
Credit risk analytics, fraud detection, SME scoring, debt burden analysis, and financial AI for banking.
Measures how much a metric deviates from its normal year-over-year pattern using a difference-in-differences approach with two-way ANOVA significance testing. Helps businesses prove whether a change was truly abnormal or just normal variation.
AI-powered due diligence engine that analyzes companies using public data, news, websites, and documents. Combines automated research, sentiment analysis, risk scoring, and AI-generated reporting into one system.
Fraud and scam detection system that analyzes emails, SMS, links, domains, and suspicious text. Combines rule-based checks, domain intelligence, link analysis, and AI interpretation to identify threats early.
Machine learning credit scoring system that evaluates the creditworthiness of small and medium enterprises using financial, behavioral, and business-related indicators moving beyond manual judgment.
AI system that uses facial emotion recognition and GPT-style analysis to interpret emotional signals in fraud detection, risk reviews, customer interaction analysis, and suspicious behavior monitoring. Processes real-time video and outputs probability scores through a dashboard interface.
AI-powered brain tumor detection system using deep learning neural networks to analyze medical imaging. Classifies tumors as benign or malignant, supporting early diagnosis and clinical decision-making with high accuracy.
Autonomous AI trading agent that analyzes market data, identifies patterns, and executes algorithmic trading decisions using machine learning and reinforcement learning. Built to operate across financial markets with real-time signal processing and risk management.
Analyzed DBR behavior in Bahrain to understand salary levels, financing obligations, repayment capacity, and credit risk interactions supporting policy-level understanding of affordability and borrowing behavior.
Built a simulation model using 500,000 probable outcomes to estimate a wide range of possible revenue scenarios under uncertainty moving beyond single-point predictions.
Segmented customers for a marketing campaign using unsupervised ML to improve targeting, lead generation, and campaign performance.
Developed ML prediction model and derivative analysis to estimate future revenue and study rate-of-change momentum detecting early signs of slowdown or acceleration.
Conducted research to help startups validate ideas, identify market gaps, build stronger business models, and prepare for investors or stakeholders.
Analyzed delivery app data to understand customer behavior, order patterns, delivery performance, and business improvement opportunities using EDA and statistical methods.
University of Sunderland
Massachusetts Institute of Technology
UC Berkeley, California
Harvard University
USAII
First certified in BahrainPresented on credit bureau innovation, cloud transformation, and AI/data-driven development within the financial and credit ecosystem.
Participated in a panel discussion on the role of AI in transforming business, innovation, and future digital strategies.
Delivered and developed AI-focused training content explaining how organizations can use AI, automation, data analysis, and machine learning to improve decision-making and business performance.
Developed academic and applied research across data science, secure databases, machine learning, statistical analysis, and business intelligence.
Open to collaborations, consulting, speaking engagements, and new opportunities.