AI/ML Quant Modeling Vice President - JP Morgan, Bengaluru

Job Description

The Vice President- BB Risk Model lead will be a key member of the Quantitative Modeling team. The successful candidate will be responsible for end-to-end management of set of BB collection and recovery models that are used for Risk Management in CCB Risk. The candidate will build a solid understanding of various Business lending products and key risk drivers for statistical credit models of those products. Job Responsibilities: Manage end-to-end model development process, including data sourcing, manipulation, exploratory data analysis and pattern discovery, documentation, assisting with implementation, and performance monitoring. Understand/evaluate efficiency of modeling techniques including both classical statistical modeling approaches and modern machine learning approaches to enhance existing models and tackle challenging modeling problems. Collaborate with cross functional partners in Risk, Finance, Technology, Model Governance throughout the entire modeling life cycle. Required qualifications, capabilities, and skills Advanced degree in a quantitative discipline (e.g., Mathematics, Statistics, Economics, Computer Science) with 9+ years of relevant working experience; Doctoral degree is preferred. Strong data analysis and statistical/economic modeling experience, such as generalized linear models, multivariate analysis, and time series analysis. Proficiency in advanced analytical languages (e.g., Python); Familiarity with framework of machine learning pipeline (e.g., tensor flow, scikit-learn); experience on the cloud platform (e.g., AWS) Demonstrated expertise in data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). GPU experience is desired. Strong communication skills to present to and collaborate with business partners and model end-users. Strong organizational and multi-tasking skills with demonstrated ability to manage expectations and deliver quality results on time. Knowledge of credit industry collection and recovery or similar experience in past is very useful