Job Description As part of the Group Risk Management (GRM) – Credit Strategy team, the Credit Strategy and Data Science Manager role will analyze, design, optimize and implement solutions to enable real‑time decision making for the retail lending businesses.The team specializes in leveraging large data sets to generate insights and make fact‑based decisions on how to profitably grow loan originations by balancing risk, pricing, operational efficiency and customer impact.The role employs a range of leading‑edge technologies and capabilities, applying machine learning and statistical techniques, to build predictive behavioral models and credit strategies.ResponsibilitiesDevelop quantitative credit risk metrics and tools to help manage, measure, and monitor credit risk.Use empirical, statistical and machine‑learning techniques to build financial models to simulate financial outcomes of strategy decisions, measure true outcomes against expectations and drive continuous improvement from findings.Design automated credit strategies to improve real‑time adjudication and the quality and consistency of manual adjudication.Support the implementation and ongoing integrity of credit strategies within appropriate systems.Engage cross‑functional stakeholders across Product, Sales, Operations and Risk Management.End‑to‑end project management, from idea conception, solution design, stakeholder buy‑in to implementation and monitoring.Qualifications Must‑haveBachelor or Master’s degree in Computer Science, Statistics, Mathematics, Economics, Engineering, or other quantitative field of study.Expert critical thinker and problem‑solver.Proficient in Excel, SQL and Python and code management best practices.Proven experience in delivering high quality and accurate work with the ability to multitask and manage priorities.Effective and conversant in both business and technical communications.Nice‑to‑haveExperience with data visualization tools such as Tableau.Familiar with retail lending business and/or credit risk concepts.Experience applying statistical concepts including linear and logistic models, gradient boosting/XGBoost, supervised statistical learning, clustering, recommendation systems, time‑series analysis, experimental design (A/B testing).Exposure to Apache Spark, Hadoop and Public Cloud technologies, data serialization (JSON, Parquet, etc).Experience with big data processing tools like Spark and Hive.What's in it for you?A comprehensive Total Rewards Program.Leaders who support your development through training and coaching.Work with a dynamic, collaborative team with ability to make a difference.#J-18808-Ljbffr