Description

Develop and implement financial risk and machine learning models in Python for regulatory stress testing and risk management.

This role is on-site.

Responsibilities

• Design and build execution workflows for models forecasting Balance Sheet Fee Revenues and calculating risk metrics under stress scenarios.
• Develop risk models in Python used by risk teams for regulatory compliance and internal risk management.
• Coordinate with functional teams to implement, test, and document financial models.
• Develop tools to monitor and analyze model performance to ensure application levels are met.
• Apply statistical and analytical tests to validate models and results.

Required Skills

• 8+ years of experience using Python for data modeling and machine learning.
• Experience with Qrm, Wolters Kluver, Axiomsl, Simcorp, Mx.3, Aladdin, and Vermeg.
• Understanding of design, development, and implementation of mathematical financial risk and ML models.
• Knowledge of advanced statistical techniques, including regression and time series analysis.
• Experience with data mining, data modeling, and machine learning techniques on financial datasets.
• Familiarity with Github for portfolio sharing.
• Bachelor's degree in a quantitative field (Computer Science, Financial Engineering, Mathematics, Data Science, or Engineering).

Key Skills

Education

Bachelor's degree