Quant jobs / MSCI

Senior Climate Machine Learning Scientist

This MSCI position develops machine-learning models for physical climate risk. The researcher trains scientific models and scales inference while checking their performance under changing climate conditions.

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.

Train climate-risk models in PyTorch or JAX.
Evaluate model performance against physical observations.
Scale inference across global geospatial datasets.
Track experiments and communicate validation results.

What they ask for

Required

Scientific machine learning
Climate-risk modelling
Python, R, C/C++ or Fortran
Geospatial formats
Climate-scenario knowledge
Model validation

Education: PhD in a relevant science, geography, engineering, climate or mathematics subject.
Experience: At least two years developing products to meet client needs.
Languages and tools: Python
R
C++
Fortran
PyTorch
JAX
MLflow
Weights & Biases

How to prepare

Train a weather or climate model with a separate validation period.
Compare regional prediction errors and extremes.
Build an experiment-tracking and inference pipeline for geospatial data.

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.