Duties: The Director, Quantitative Model Validator position will report directly to the Head of Model Validation for AI/ML and manages a team of quantitative model validators to test and evaluate the conceptual soundness of models and assess limitations and suitability for use. Subject matter authority in the key model risk area of Artificial Intelligence/Machine Learning and other related modeling areas
Requirements: Has a proven track record with a minimum of eight years of experience with Masters (or six years with PhD) in model risk management in banking or insurance; Has a master’s degree in science, data science, math, statistics, or related area; Previous people leadership experience and shown ability to mentor and develop people; Confirmed ability to apply both strategic and analytic techniques to provide business solutions and recommendations; Ability to work independently with high initiative and comfortable working optimally in a collaborative team environment

Duties: Working on projects in partnership with business teams to identify opportunities for improvement, gather and analyze information and data, develop improvements and innovations; Apply knowledge of statistics, machine learning, programming, and data modeling to recognize patterns, identify opportunities, evaluate business opportunities, and deliver valuable insights; Effectively structure communication of insights from work streams and deliver clear and professional presentations to the team/team leaders/managers/stakeholders; Scope, size and plan DS project roadmaps with clear milestones, metrics and identify potential derailment factors. Drive end-to-end execution from incubation to production; Deliver high quality AI/ML solutions and maintain proper documentation to foster seamless workflows and code-reusability principles
Requirements: B.Tech/Bachelor's degree in a quantitative field; Quantitative field like Engineering, Technology, Computer Science, Statistics, Operations Research, Applied Mathematics, Economics; Full time 4-7 years relevant work experience in analytics/data science; Proficiency with mathematics and statistical analysis with significant hands-on experience in AI/Data Science; Proficiency in Programming Languages (Python), SQL; Knowledge in exploratory analysis, predictive modeling, prescriptive modeling, strong understanding of machine learning algorithms like Regression, Bagging (Decision Trees, RF), Boosting (XGBoost, GBM), Clustering (KMeans)