Quant jobs / MSCI

AI Engineer

MSCI’s central data AI team builds tools that improve the reliability of financial datasets. This engineering role develops anomaly-detection and language-model capabilities for internal platforms and client delivery.

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.

Build anomaly detectors for data-quality problems and drift.
Deploy NLP and generative-AI components into data platforms.
Integrate AI services with MSCI APIs and data workflows.
Test and monitor production applications with data and technology teams.

What they ask for

Required

Python
NLP
Statistical modelling
Production machine learning
Anomaly detection
Software testing

Preferred

LLM or agent projects
Azure or GCP
CI/CD experience

Education: Bachelor’s or master’s study in computing, mathematics, engineering or a related field.
Experience: Five years working with statistical models and data analysis.
Languages and tools: Python
Azure
GCP

How to prepare

Build a detector for missing and anomalous market-data observations.
Evaluate an NLP extraction pipeline against labelled examples.
Package a small model with tests and production monitoring.

What to study

The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

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

View the posting on MSCI’s site ↗

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