Available for Consulting & Advisory

Abiola Osho

VP, Applied AI & ML Lead — JPMorgan Chase

Machine learning engineer and applied scientist with deep expertise in financial services, fraud prevention, and large-scale ML systems. Building production-grade AI that protects billions in annual transaction volume across Zelle, ACH, Wires, and BillPay.

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About

Building AI that matters

Combining scientific rigor with strong engineering execution to deliver measurable impact in high-stakes financial environments.

ML Engineering at Scale

Designs, builds, and productionizes fraud detection models across distributed environments using Python, Spark, AWS, and graph ML — processing millions of transactions in real time.

Fraud Intelligence Architecture

Architects next-generation fraud platforms using graph networks and LLM pipelines to extract semantic fraud indicators from unstructured transaction data.

Responsible AI & Governance

Establishes model governance aligned with SR 11-7, OCC, and FRB standards — embedding fairness assessments and responsible AI principles enterprise-wide.

Cross-Functional Leadership

Partners across product, engineering, and risk teams to shape data strategy, define KPI frameworks, and translate complex findings into actionable insight for senior leadership.

Abiola Osho is a hands-on machine learning engineer and applied scientist currently serving as Vice President, Applied AI & ML Lead at JPMorgan Chase in Jersey City, NJ. She leads enterprise-scale AI initiatives across Zelle, ACH, Wires, BillPay, and Business Payments — protecting billions of dollars in annual transaction volume.

Her work spans the full ML lifecycle: from feature engineering and model development to cloud migration, drift monitoring, and regulatory-aligned governance. She holds a Ph.D. in Computer Science from Kansas State University and brings research depth in graph analysis, NLP, and privacy-preserving systems. Prior to her current role, she built identity-linkage infrastructure at PayPal supporting over 40 billion daily events.

Experience

Professional Journey

A career built at the intersection of academic research and enterprise-grade engineering.

VP, Applied AI & ML Lead
Feb 2025 – Present
JPMorgan Chase & Co. · Jersey City, NJ
  • Led enterprise-scale AI and product analytics initiatives across Zelle, ACH, Wires, BillPay, and Business Payments, driving strategy, roadmap execution, and measurable improvements in customer protection.
  • Directed product analytics to uncover user behavior patterns, friction points, and performance drivers — defining success metrics, KPI frameworks, and forecasting models.
  • Architected next-generation fraud intelligence platforms using graph networks and LLM pipelines to extract semantic fraud indicators from unstructured transaction memos.
  • Led and developed a high-performing data science team, establishing standards for feature engineering, model documentation, fairness assessments, and Responsible AI adoption.
Applied AI & ML Senior Associate
Jul 2022 – Jan 2025
JPMorgan Chase & Co. · Jersey City, NJ
  • Developed and enhanced ML models to detect and prevent fraud across high-volume digital transaction channels — significantly improving risk mitigation across Zelle, ACH, Wires, and BillPay.
  • Engineered a dynamic fraud feedback pipeline for check fraud models, integrating multi-channel indicators including ATM activity, IP anomalies, routing behavior, and branch metadata.
  • Led cloud modernization of fraud risk models, migrating on-prem systems to AWS (S3, SageMaker, EC2) and optimizing reliability and scalability.
  • Built quarterly model monitoring frameworks to track drift, decay, and emerging fraud vectors, driving model refresh cycles and strategic tuning.
Graduate Research Assistant
Aug 2017 – May 2022
Kansas State University · Computer Science · Manhattan, KS
  • Engineered scalable real-time data pipelines to extract and analyze large-scale social media and Twitter REST API data for behavioral analysis and threat detection.
  • Designed ML frameworks leveraging graph analysis, NLP, Random Forests, and sentiment modeling to predict threat exposure and detect users vulnerable to abuse and misinformation.
  • Designed a privacy-preserving system using dynamic noise injection to mitigate inferential attacks while preserving platform usability.
Applied AI & ML Summer Associate
Jun 2021 – Aug 2021
JPMorgan Chase & Co. · New York (Virtual)
  • Performed community detection on large-scale Zelle graphs comprising 75M profiles and 55M edges using TigerGraph and NetworkX.
  • Integrated graph features into fraud models trained on 5M transactions, improving AUC by 5%.
Software Engineer Intern — Data Platforms
May 2020 – Aug 2020
PayPal Inc. · San Jose (Virtual)
  • Designed and implemented the Lighthouse 360 ID — a universal analytics identifier system for over 40 billion daily client-side events, enabling unified user tracking across devices and login states.
  • Designed hybrid real-time/batch data architectures leveraging Spark, Kafka, and BigTable for identity linkage and risk analytics.
Education

Academic Foundation

Ph.D., Computer Science
Kansas State University · Manhattan, KS
May 2022
M.S., Computer Science
University of Ibadan · Ibadan, Nigeria
Feb 2016
B.S., Computer Engineering
Olabisi Onabanjo University · Ago-Iwoye, Nigeria
Sep 2010
Expertise

Skills & Impact

A comprehensive toolkit spanning research, engineering, and enterprise leadership.

Core AI / ML
Fraud Modeling Graph ML LLM Integration NLP & Feature Engineering Supervised Learning Deep Learning XGBoost / SHAP Scikit-Learn Sentiment Modeling
Languages & Tools
Python PySpark SQL GSQL NetworkX TigerGraph Kafka Spark
Cloud & Platforms
AWS SageMaker AWS EC2 / S3 Databricks Snowflake BigTable Distributed Computing
Risk & Governance
Model Validation Responsible AI SR 11-7 Compliance OCC / FRB Alignment Data Governance MLOps Model Monitoring
Leadership & Strategy
Team Management AI Product Strategy Stakeholder Influence Executive Communication AI Product Roadmaps Cross-Functional Leadership Data Architecture
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Billions ProtectedDirected AI initiatives protecting billions in annual transaction volume across major money movement channels at JPMorgan Chase.
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Graph-Scale Fraud DetectionBuilt multi-product fraud models leveraging Graph ML and LLMs on graphs of 75M+ profiles and 55M+ edges.
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Cloud ModernizationDrove cloud migration of fraud systems to AWS, raising reliability and enabling scalable monitoring and retraining pipelines.
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Privacy-Preserving AIDesigned systems injecting dynamic noise to mitigate inferential attacks while preserving platform usability — at PayPal scale.
Contact

Let’s Connect

Interested in AI strategy, fraud intelligence, or building something meaningful together? Reach out — I’d love to talk.

drabiolaosho@gmail.com LinkedIn