Quant jobs / UBS

Quantitative Engineer – GM Distribution Quant Analytics

Lead development of analytics and AI tools for UBS Global Markets Distribution. The role owns financial data, model implementation and production services used by sales and trading colleagues.

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.

Frame desk problems and choose suitable statistical or AI methods.
Build data pipelines and deploy analytical applications.
Monitor service quality, operating cost and release behaviour.
Guide engineering work while remaining hands-on.

What they ask for

Required

Python and production engineering
Financial data engineering
LLM or agentic application experience
Technical team leadership
Clear communication

Preferred

Kubernetes, Airflow, Azure, Spark or kdb+/q
Global Markets workflow knowledge

Education: Four-year degree in mathematics, computing, engineering or a related subject; master’s preferred.
Experience: Ideally at least 7 years leading teams while delivering quantitative engineering.
Languages and tools: Python
Markets: multi-asset

How to prepare

Build a financial-data service with quality checks and monitored deployment.
Compare a simple statistical baseline with an LLM approach on a defined desk task.
Explain release, cost and data-governance controls.

What to study

The HFT Quant Developer 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.