We are seeking a highly skilled Python Developer with strong experience in credit risk analytics and quantitative modeling to join our dynamic risk management team. The ideal candidate will combine programming expertise with a deep understanding of financial risk, providing robust models, automation, and analytical tools to support data-driven decision-making.
Develop, maintain, and optimize Python-based applications for credit risk modeling, scoring, and reporting.
Design and implement quantitative models to measure, monitor, and predict credit risk across portfolios.
Collaborate with risk analysts, quants, and data scientists to translate business requirements into scalable software solutions.
Conduct data analysis and validation, ensuring high-quality inputs for risk models.
Automate risk reporting, stress testing, and scenario analysis workflows.
Implement best practices in coding, version control, and model governance.
Stay current with industry trends in credit risk, regulatory requirements, and quantitative finance.
Strong Python programming skills, including experience with libraries such as pandas, NumPy, SciPy, scikit-learn, or PyTorch.
Solid experience in credit risk modeling (e.g., PD, LGD, EAD models, Basel II/III frameworks).
Strong quantitative and statistical skills, including regression, time series, or machine learning techniques.
Familiarity with databases and SQL for data extraction and manipulation.
Experience with version control (Git) and software development lifecycle best practices.
Excellent problem-solving skills and the ability to translate complex quantitative concepts into actionable solutions.
Advanced degree in Mathematics, Statistics, Financial Engineering, Economics, or related field.
Knowledge of regulatory requirements related to credit risk (e.g., Basel, IFRS 9).
Exposure to cloud computing platforms (AWS, GCP, Azure) or big data tools (Spark, Hadoop).
Experience in backtesting, model validation, or risk automation frameworks.