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UBS · United States

# Quantitative Engineer – GM Distribution Quant Analytics

United States Data & Analytics 

Listed on UBS’s official careers site. Listed as of October 20, 2026. Check that it is still open and apply there.
[Apply on UBS’s careers site →](https://jobs.ubs.com/TGnewUI/Search/home/HomeWithPreLoad?partnerid=25008&siteid=5012&PageType=JobDetails&jobid=352614)

[OpenNash Jobs](https://jobs.opennash.com/) / [UBS](https://jobs.opennash.com/companies/ubs/) / Quantitative Engineer – GM Distribution Quant Analytics

## Job description

Do you want to build analytics and AI solutions that directly influence Global Markets Distribution? Are you excited by solving real business problems with the right technology, whether that’s a simple statistical model or a modern LLM-based system? Do you enjoy turning ideas into production tools that win business and save people time?

We are looking for a Quantitative Engineer to:
- lead a team while remaining a hands-on engineer, owning implementation, data quality, monitoring, cost, and releases
- solve real Distribution problems by assessing the data, selecting the right approach, building solutions, and putting them in front of users
- design, build and run analytics and AI services end to end, from data and models through to the tools people use
- partner directly with sales and trading desks, quants, and developers to frame problems and deliver working solutions
- choose the right technique for each problem, reusing existing solutions where appropriate
- raise engineering standards and help grow the team as we continue to hire

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Oct 20, 2026
- Last checked: Oct 20, 2026
- Listed for: Under a day
- Location: United States[Apply on UBS’s careers site →](https://jobs.ubs.com/TGnewUI/Search/home/HomeWithPreLoad?partnerid=25008&siteid=5012&PageType=JobDetails&jobid=352614)

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OpenNash lists jobs from employers’ own careers sites and does not handle applications.
Dates come from the employer where stated; “first seen” and “last checked” are our own observations.

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