Quant jobs / BNP Paribas

Artificial Intelligence & Model Risk Auditor

Assess AI and model risks as part of BNP Paribas’s internal audit team. The technical work examines predictive performance, fairness and robustness across conventional machine learning and language models.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Test model behaviour across development, deployment and monitoring stages.
Review accuracy, calibration, stability and fairness measures.
Assess hallucination and bias risks in language-model use cases.
Document weaknesses and evaluate remediation evidence.

What they ask for

Required

Applied data science or AI research
Model-audit frameworks
Python or R
Machine-learning and NLP libraries
Technical communication

Preferred

Internal audit certification
US and Canadian banking regulation

Education: Bachelor’s or master’s in mathematics, statistics, computing, financial engineering or another quantitative subject.
Experience: At least three years applying data science, AI research or ML engineering to language models and other modelling methods.
Languages and tools: Python
R
TensorFlow
PyTorch
scikit-learn
pandas
Polars
NumPy
GitHub

How to prepare

Build an evaluation set covering calibration and subgroup error rates.
Test an LLM workflow for hallucination and unsupported answers.
Write a reproducible audit finding with measurable remediation criteria.

What to study

The Model Validation Quant roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on BNP Paribas’s site before applying: requirements, location and pay can change after we check them.

View the posting on BNP Paribas’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with BNP Paribas and doesn’t handle applications.