Senior Quant Model Risk Auditor: AI/ML Controls

JPMorganChase; Paris; Sur place; EUR 110 000 - 150 000; Plein temps

Résumé du poste

J.P. Morgan in Paris seeks a Quant Model Risk Auditor within Internal Audit to assess highly technical model risks and controls across diverse products.

You will engage with senior PhD quants and leaders, challenging and improving model risk management throughout the lifecycle. You will develop and implement advanced AI/ML solutions for model risk control assessments, informing strategic decisions on the firm’s risk profile while improving testing rigor and efficiency.

Qualifications

• PhD or Masters in Mathematics, Data Science, QF, CS, AI/ML or related field.
• Experience in AI/ML product development or model validation preferred.
• Strong programming and architectural understanding of AI/ML solutions.
• Excellent written and verbal communication for technical reports.

Responsabilités

• Perform highly technical reviews of complex models across lines of business.
• Assess model risk and ensure appropriate mitigation by controls.
• Evaluate model performance programs and monitoring thresholds.
• Lead end‑to‑end model risk control assessments and fieldwork.
• Partner with PhDs and leaders to address control gaps and allocate resources.
• Develop AI/ML product solutions to inform strategic decisions.
• Validate remediation actions addressing regulators' findings.

Connaissances

PhD/Masters STEM

AI/ML experience

Python/R

Communication skills

Risk/control mindset

Leadership

Formation

PhD or MSc in STEM

Outils

LangChain

Python

R

LangGraph

Description du poste

J.P. Morgan in Paris seeks a Quant Model Risk Auditor within Internal Audit to assess highly technical model risks and controls across diverse products.

You will engage with senior PhD quants and leaders, challenging and improving model risk management throughout the lifecycle. You will develop and implement advanced AI/ML solutions for model risk control assessments, informing strategic decisions on the firm’s risk profile while improving testing rigor and efficiency.