Quantitative Risk Engineer: AI-Driven Analytics in Cloud

swissQuant; Zürich; Vor Ort; CHF 140.000 - 220.000; Vollzeit

Zusammenfassung

swissQuant Group is building a state-of-the-art cloud-based portfolio risk system for leading financial institutions. You will join a cross-functional team of quant engineers and developers to design, implement, test and monitor risk analytics across the full lifecycle.

The role emphasizes expanding coverage of risk factors, improving accuracy and performance, and integrating AI-assisted tooling to accelerate development while ensuring model quality and regulatory compliance.

Qualifikationen

• Advanced degree in quantitative field with strong modeling skills.
• Solid experience in quantitative model development, testing and documentation.
• Strong knowledge of financial markets, products, and risk analytics.
• 3+ years of Python programming experience.
• Experience with SQL and/or NoSQL datastores.
• Familiarity with CI/CD tools and cloud environments.

Aufgaben

• Design and deliver production-grade risk models and code.
• Implement and monitor risk analytics across the development life cycle.
• Expand instrument, risk-factor, and analytics coverage.
• Enhance portfolio risk and performance analytics tools.
• Communicate results to clients and stakeholders.
• Collaborate on regulatory-compliant model development and validation.

Kenntnisse

Python programming

Statistical modelling

Machine learning

Communication skills

Client-facing experience

Teamwork

Ausbildung

Master's or PhD in quantitative field

Tools

SQL databases

NoSQL databases

Google Cloud

Jenkins

Docker

Kubernetes

Claude Code

Codex

Gemini CLI

Cursor

Windsurf

Jobbeschreibung

swissQuant Group is building a state-of-the-art cloud-based portfolio risk system for leading financial institutions. You will join a cross-functional team of quant engineers and developers to design, implement, test and monitor risk analytics across the full lifecycle.

The role emphasizes expanding coverage of risk factors, improving accuracy and performance, and integrating AI-assisted tooling to accelerate development while ensuring model quality and regulatory compliance.