Duties: Develop and implement AI/ML models and algorithms to solve business problems; Collaborate with cross-functional teams to understand requirements and translate them into technical solutions; Train and evaluate AI/ML models using large datasets; Apply advanced statistical and ML techniques, including predictive modeling, time series analysis, and optimization algorithms, to extract insights from complex data sets; Optimize and fine-tune AI/ML models for performance and accuracy; Design and develop data pipelines to preprocess and transform data for AI/ML models
Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field; 6-9 years of demonstrated experience in applied AI/ML engineering; Strong programming skills in Python, with experience in developing and maintaining production-level code; Proficiency in working with large datasets and data preprocessing; Solid understanding of AI/ML algorithms and techniques, including deep learning, time series forecasting and natural language processing

Duties: Obtain and conduct QA/QC on all data required for CCAR/CECL model development; Develop segment and/or account level CCAR/CECL stress loss models; Perform all required tests (e.g. sensitivity and back-testing); Validate/recalibrate all models annually to incorporate latest data. Redevelop as needed; Deliver comprehensive model documentation; Work closely with cross functional teams, including country/region’s business stakeholders, model validation and governance teams, and model implementation team; Prepare responses/presentations to regulatory agencies on all CCAR/CECL models built
Requirements: Advanced Degree (Bachelors required, Masters/PhD preferred) in Statistics, Applied Mathematics, Operations Research, Economics, MBA (Finance), or other highly quantitative discipline; 7-15 years’ experience in performing quantitative analysis, statistical modeling, loss forecasting, loan loss reserve modeling, and particularly econometric modeling of consumer credit risk stress losses; Experience with dynamics of unsecured or secured products a strong plus; Active role in performing some analytical components of an econometric modeling-driven stress loss process (data collection, data integrity QA/QC/reconcilements, pre-processing, segmentation, variable transformation, variable selection, econometric model estimation, sensitivity testing, back testing, out-of-time testing, model documentation, and model production implementation)