Ideate, develop and test quantitative modelling and measuring techniques
Support analytics including automation and data engineering
Present ideas and results to leadership team
Cover model lifecycle including development, monitoring, recalibration, and implementation of risk models in R/Python
Research and evaluate quantitative approaches for model development; coding, testing and validation
Assist team members in statistical model development, monitoring, implementation and model validation for Operational Risk models (AMA capital estimation, stress testing, capital allocation etc)
Improve efficiencies and modelling activities that require enhancements for execution, standards and maintenance of risk models
Pitch, develop, and test Machine Learning and AI models and compare performance against legacy statistical models
Use techniques including OLS and Logistic Regression, Time Series Regression, Monte Carlo Simulation, Decision Trees, Gradient Boost, Bayesian Inference
Requirements
2-3 years of experience
Sound knowledge of Probability and Statistics
Experience in Data Science Projects (Statistical Modelling/Machine Learning)
Programming in R/Python
Knowledge/Interest in Banking and Financial Services