QIS Platform Analyst at UBS in Location not listed

Are you interested in a role that offers high visibility, significant autonomy, and the opportunity to apply AI in real production environments within a front office adjacent setting?

As a QIS Platform Analyst, you will operate at the intersection of Python engineering, data, AI and front office processes, with direct ownership of platform analytics, intelligent monitoring and automation capabilities. The role is deliberately multi disciplinary, requiring the ability to handle a broad spectrum of problems spanning technology, data, business logic and controls.
This role offers high visibility, significant autonomy, and the opportunity to apply AI in real production environments within a front office adjacent setting.
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Key responsibilities:
Python engineering (core responsibility)
o Design, develop and maintain production quality Python code supporting QIS platform analytics, controls, monitoring and workflow automation
o Build scalable, resilient data pipelines and validation frameworks used in live environments
o Apply strong software engineering discipline: testing, code review, documentation and maintainability
• AI & machine learning
o Contribute directly to AI / ML initiatives across the QIS platform, including intelligent monitoring, anomaly detection, signal identification, and automation of control processes
o Work with both structured and unstructured datasets to enable AI driven platform capabilities
o Partner with quants, data scientists and engineers to industrialise AI concepts from prototype into production
• Platform monitoring & resilience
o Design and enhance automated checks, dashboards and observability frameworks
o Lead investigations into data or production anomalies and drive resolution across teams
o Continuously improve platform resilience, transparency and scalability
• Multi disciplinary problem solving
o Handle a plethora of disciplines simultaneously: coding, data analysis, platform design, process optimisation and stakeholder coordination
o Break down complex, ambiguous problems spanning systems, data and business logic
o Drive durable, structural improvements rather than short term fixes
• Change & platform evolution
o Support onboarding of new strategies, features, datasets and analytical capabilities
o Contribute to re platforming and modernisation initiatives
o Identify opportunities to improve automation, developer tooling and operational efficiency
• Stakeholder engagement
o Work closely with Trading, Structuring, Model Quants and Technology teams
o Translate business problems into robust technical solutions and communicate outcomes clearly to senior stakeholders