Fraud Modeling VP – Applied AI Leader

Aumni

Greater London

Vor Ort

GBP 180.000 - 260.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Zusammenfassung

JPMorganChase is seeking an Applied AI Machine Learning VP (Fraud Modelling) within the ICB Risk Modelling team in London. You will lead the development and management of advanced ML models to mitigate fraud risk, collaborating with Strategy, Technology, Legal, Compliance, and governance functions to meet stringent regulatory standards.

Primary focus is identity verification fraud; you will evaluate vendor models, ensure robust deployment, and maintain audit-ready artifacts for internal audits

Qualifikationen

  • Advanced degree in a quantitative or technical discipline.
  • Strong background in fraud modelling and regulatory considerations.
  • Experience with applied data science and ML techniques.
  • Proficiency in Python/SQL and production-grade code.

Aufgaben

  • Develop and manage proprietary fraud models and vet vendor models.
  • Prepare governance and Model Risk Management documentation.
  • Monitor performance and drift; define monitoring metrics.
  • Communicate design, trade-offs, and results to senior stakeholders.
  • Maintain audit-ready documentation and respond to inquiries.

Kenntnisse

Python
SQL
NumPy
Pandas
Scikit-Learn
Generative AI
Communication
Machine learning
Team collaboration

Ausbildung

MSc or PhD in quantitative/technical field

Tools

Git

Jobbeschreibung

JPMorganChase is seeking an Applied AI Machine Learning VP (Fraud Modelling) within the ICB Risk Modelling team in London. You will lead the development and management of advanced ML models to mitigate fraud risk, collaborating with Strategy, Technology, Legal, Compliance, and governance functions to meet stringent regulatory standards.

Primary focus is identity verification fraud; you will evaluate vendor models, ensure robust deployment, and maintain audit-ready artifacts for internal audits

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