Quant jobs / UBS

Data Scientist, Investment Bank

Develop AI applications within UBS’s investment-bank delivery team. The work combines model evaluation and Python engineering with business, quantitative and risk stakeholders.

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.

Prototype AI solutions for selected bank use cases.
Integrate models with data sources and enterprise interfaces.
Evaluate reliability and maintain testing and monitoring.

What they ask for

Required

Hands-on AI or machine-learning implementation
Python programming
Model evaluation
Structured and unstructured data
Cross-team communication

Preferred

Financial markets, pricing or risk experience

Education: Bachelor’s or master’s in a STEM or quantitative subject, or equivalent practical experience.
Experience: Relevant professional, academic or internship AI implementation experience; no minimum years stated.
Languages and tools: Python

How to prepare

Build a RAG application with a labelled evaluation set.
Add tests, deployment and monitoring to a Python model service.
Compare measured benefits and failure modes against a simple baseline.

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 UBS’s site before applying: requirements, location and pay can change after we check them.

View the posting on UBS’s site ↗

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