Mohammed Hesham Ahmed
Data Science @ Global Payments x Worldpay | Agentic AI | Data → Product Value | Fintech
- Role
- Data Scientist Ii at Global Payments Inc.
- Location
- Boston, MA, US
- LinkedIn followers
- 500 followers
About Mohammed Hesham Ahmed
I’m a data scientist specializing in intelligent payment systems, combining machine learning, statistical inference, and agentic AI to optimize authorization rates, detect fraud, and unlock revenue in high-scale transaction environments.Over 5+ years, I’ve partnered with cross-functional teams to architect solutions across the ML spectrum: supervised and unsupervised learning for retry optimization, dynamic routing, expired card handling, payment response classification, and anomaly detection. I lead end-to-end initiatives from problem framing through insight delivery, translating payment mechanics into probabilistic inference tasks that drive measurable business outcomes.Recent impact: Delivered $18M+ in revenue optimization, reduced manual review queues by 87% through agentic AI automation, and achieved +79bps authorization lift via payload editing and feature engineering for ML models. Work recognized by CODiE and Tekne Awards.Currently exploring how autonomous AI systems, combining LLMs, dynamic tool-calling, and reasoning workflows that can augment root cause analysis, experimentation loops, and production ML monitoring in payment optimization environments
Experience
Data Scientist Ii
May 2025 — Present · Boston, MA, US
Worldpay acquired by Global Payments.• Payment Payload Optimization – Engineered data-driven framework analyzing outgoing payment payloads to identify data quality issues driving authorization failures. Unlocked additional lift of 79bps translating to $10.9M additional annual revenue through standardization strategies across merchant portfolio.• Agentic AI for Payment Response Classification – Architected autonomous reasoning system combining TF-IDF classification (90.7% baseline) with agentic AI workflows (using Open AI Agents SDK and Crew AI) featuring dynamic similarity search and explainable predictions. Reduced manual review queue by 87%(45K → 6K daily cases) while achieving 95% overall accuracy. Projected $7.5M annual cost savings through intelligent automation.• Built automated ML framework accelerating root cause analysis for payment investigations. System evaluates feature impact rankings enabling faster diagnosis of authorization degradation, processor performance shifts, and merchant specific anomalies.• Partial Payment Retry Strategy – Developed proof-of-concept using survival analysis and Monte Carlo simulation for partial payment retries on recurring subscriptions, modeling optimal retry timing. Identified a potential 8% uplift in recovery rates (~$4M annual impact) through improved handling of insufficient funds cases.• Product Strategy & Roadmap – Shaped product priorities through payment analytics insights, translating processor performance analysis and optimization opportunities into revenue-generating product features decisions.
Education
Osmania University
Bachelor of Engineering, Electronics and Communication Engineering (ECE)
2013 — 2017
University of St. Thomas
Master of Science - MS, Data Science
2019 — 2021
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