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Tier-1 brand and metro location increase applicants, while niche quant skills moderate competition overall.
Highly domain-specific banking capital and ECL expertise reduces cross-industry transferability.
Explicit Python/C++, quantitative and banking requirements create moderately strict shortlisting filters.
Develop and maintain quantitative analytics and forecasting tools for capital, expected credit loss, risk-weighted assets, and financial resource management.
Translate business and regulatory requirements into robust Python and C++ implementations with strong control, data quality, and documentation standards.
Collaborate with global stakeholders across Front Office, Credit Risk, Finance, and Technology to deliver analytical solutions and support model development and production implementation.
At least 2 years of hands-on Python development experience including data analysis and production-quality coding.
Working knowledge of C++ including object-oriented programming and common data structures.
Experience with relational databases and SQL; knowledge of Oracle or MySQL database design preferred.
Strong academic background in engineering, computer science, mathematics, statistics, physics, finance, economics, or another quantitative discipline.
Experienced in quantitative analytics within banking or financial services domains related to credit risk and regulatory capital.
Comfortable working collaboratively across business, risk, finance, and technology teams in a global, fast-paced environment.
Able to manage competing priorities and apply structured problem-solving to complex regulatory and business requirements.