Quant Analytics Engineer — AI & Production Solutions

UBS; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

UBS invites you to join the Quant Analytics team in Global Markets Distribution in London as a Senior Quantitative Engineer. You will design, build and run analytics and AI services end-to-end, from data and models through to production tools used by the desk.

You're expected to have a strong Python background, 3+ years in production engineering, and hands-on experience with modern AI tooling and large financial datasets.

Qualifications

• Degree in mathematics, computer science, engineering, or related field; master’s degree preferred.
• 3+ years as hands-on Quantitative Engineer delivering production solutions.
• Strong Python skills and production engineering practices (testing, version control, code review, and sound software architecture).

Responsibilities

• Own 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.

Skills

Python

Production engineering

AI tooling

Cloud deployment

Data engineering

Kubernetes

Education

Math/CS/Engineering degree

Master’s degree preferred

Tools

Kubernetes

Apache Airflow

Microsoft Azure

Apache Spark

kdb+/q

Job description

UBS invites you to join the Quant Analytics team in Global Markets Distribution in London as a Senior Quantitative Engineer. You will design, build and run analytics and AI services end-to-end, from data and models through to production tools used by the desk.

You're expected to have a strong Python background, 3+ years in production engineering, and hands-on experience with modern AI tooling and large financial datasets.