Quantitative Risk Modeler (Python)

As a successful candidate you will be given an opportunity to acquire and develop knowledge from related fields
Collaborate with stakeholders throughout the organization to develop project plans of delivering objects and timelines of model development and implementation
Develop risk models in Python R used by risk teams for regulatory stress testing submission and company risk management Design and build the execution workflow of models to forecast Balance Sheet Fee Revenues Macroeconomic Factors
Expense and calculate risk metrics under various stress scenarios sensitivity attribution analysis
Coordinate with different functional teams to implement models and coordinate coding testing implementation and documentation of financial models
Develop processes and tools to monitor and analyze model performance to ensure the expected application performance levels are achieved Also apply various statistical and analytical tests for validating models and results
Develop presentation decks using visual analytics tools and techniques Jupyter Hub Python
Apply data mining data modelling and machine learning techniques to analyze large financial datasets and enhance the model performance

Qualifications
Undergraduate Degree in a quantitative field computer science financial engineering mathematics data science or engineering
Experience using one or more programming languages Python
Excellent written and verbal communication skills for coordination across teams
Understanding of design development and implementation of mathematical financial risk and ML models
Relevant work experience in a related field based on education level
Knowledge of advanced statistical techniques and concepts regression time series analysis statistical tests etc