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Tier-1 brand and metro location increase competition, but credit-risk specialization reduces applicant density.
Specialized credit-risk and IFRS9 requirements strongly limit cross-industry transferability.
Regulatory IFRS9 context, mandatory credit-risk data expertise and specific tool expectations enforce strict screening.
Construct and manage modelling and monitoring databases for regulatory credit risk (PD, LGD, EAD) and IFRS9 provision models.
Lead data quality management including root-cause analysis and resolving data quality issues with minimal supervision.
Lead process governance meetings and contribute to project enhancements, liaising with modelling and IT stakeholders.
Master's or PhD in quantitative fields like Mathematics, Physics, Economics, Finance, or Engineering, or equivalent performance track record.
Proven expertise in data wrangling including cleaning, organizing, and transforming raw data.
Strong programming skills in Python; exposure to SAS and/or R desired.
Familiarity with credit risk model data used for PD, LGD, and EAD model creation.
Experienced in handling intermediate-level data wrangling challenges independently.
Capable of leading modelling and monitoring database creation and managing change request projects with minimal supervision.
Able to lead process governance and knowledge sharing within expert peer teams.