Python Developer Credit Risk & Quantitative Analytics

Salary is 170k to 200k + bonus

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.

Key Responsibilities:

• 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.

Required Qualifications:

• 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.

Preferred Qualifications:

• 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.