Job responsibilities:
- Drive synergy in model performance tracking across different sub-lines of business.
- Enhance model performance framework to holistically capture model health, providing actionable insights to model users.
- Collaborate with model developers to identify potential opportunities for model calibration and conduct preliminary Root Cause Analysis in case of model performance decay.
- Design and build robust framework to monitor quality of model inputs.
- Explore opportunities to drive efficiency in model inputs and performance tracking through use of Large Language Model (LLM).
- Partner with teams across, Risk, Technology, Data Governance, and Control to support effective model performance management and insights.
- Deliver regular updates on model health to senior leadership of risk organization and the first line of defense.
Required qualifications, capabilities, and skills
- Advanced degree in Mathematics, Statistics, Computer Science, Operations Research, Econometrics, Physics, or a related quantitative field.
- Minimum of 3 years of experience in developing and managing predictive risk models in financial industry.
- Proficiency in programming languages such as Python, PySpark, and SQL, along with familiarity with cloud services like AWS SageMaker and Amazon EMR.
- Deep understanding of advanced machine learning algorithms (e.g. Decision Trees, Random Forest, XGBoost, Neural Networks, Clustering etc)
- Strong conceptual understanding of performance metrics used to monitor health of machine learning models.
- Fundamental understanding of the consumer lending business and risk management practices.
- Experience of working with large datasets with strong ability to analyze, interpret, and derive insights from data.
- Advanced problem-solving and analytical skills, with a keen attention to detail.
- Excellent communication skills, with the ability to convey complex information clearly and effectively to senior management.
Preferred qualifications, capabilities, and skills
- Experience of data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency).
- Proven expertise in designing, building, and deploying production-quality machine learning models.
- Ability to effectively collaborate with multiple stakeholders on projects of strategic importance, ensuring alignment and successful outcomes.
- Basic level of proficiency in Tableau