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Citi is looking for a Quantitative Developer to build and advance Python-based risk analytics tools and dashboards that sit at the heart of a global stress testing programme. In this role, you will combine deep software engineering expertise with hands-on AI development to deliver solutions that directly inform how Citi measures and manages financial risk at scale. This is an opportunity to work on technically complex, high-impact problems within a collaborative team in Mumbai.
Job Responsibility
Build and enhance risk analytics tools, dashboards, and reporting capabilities that support a firm-wide stress testing programme used to assess financial resilience across global portfolios
Design and develop Python-based implementations of risk models, ensuring clean, high-performance code that meets production standards
Lead AI-driven development initiatives from prototype through to stakeholder review, translating analytical requirements into working solutions using large language models and AI-assisted tooling
Manage the end-to-end integration of risk models and analytics tools with enterprise IT systems, including user acceptance testing and production releases
Develop and maintain Stress Loss Calculator infrastructure and other core components that underpin the stress testing platform
Gather and incorporate feedback from key stakeholders to refine prototypes and ensure delivered tools meet business and analytical needs
Requirements
Master's degree in a quantitative discipline such as Mathematics, Engineering, or Computer Science
5 or more years of professional software engineering experience with Python as the primary language, ideally gained within the financial services industry
Demonstrated ability to write clean, high-performance, and idiomatic Python code that is maintainable in a production environment
Applied experience using advanced AI tools and large language models such as Gemini or Claude to design and deliver data and risk analytics solutions
Strong analytical and problem-solving skills, with familiarity across financial markets, financial instruments, and risk management methodologies
Nice to have
Proficiency with AI-powered development tools such as GitHub Copilot to accelerate code generation, debugging, and performance optimization
Familiarity with stress testing frameworks or quantitative risk modelling within a financial institution
Experience managing UAT processes and coordinating production releases for analytics or model-driven systems
What we offer
Hybrid working model with 3 days in the office and 2 days working remotely
Access to learning and development resources that support your growth as both a software engineer and a quantitative practitioner
Exposure to global risk management programmes
The opportunity to work at the forefront of AI adoption in financial services
A performance-driven environment where your technical contributions directly shape the quality and capability of critical risk infrastructure