Job description

Essential Duties and Responsibilities • Handle the delivery of multiple, complex analytics projects to clients from inception to completion • Open to hands-on modelling work • Manage (small) projects to achieve milestones /manage& motivate project team to ensure quality deliverables • Mentor and coach junior team members in model development process • Drive high standards of customer service and proactive client communication throughout the project term • Proactively communicate project status and deliverables through the life of the project • Ensure issues raised by the client are resolved in a timely and efficient manner • Prepare and deliver structured model development/validation training courses to the SEA clients • Flexible to travel to client locations on short notice for project delivery(30%) Knowledge & Experience • Up to 0-5 years' experience in credit risk modelling across customer life cycle • Expert in any two analytical programing languages SAS,Python and R • Comfortable with large data sets • Thorough understanding of the Application/Behaviour/Collection scorecard development process. • Thorough knowledge of the use and application of analytical methodologies Linier Regression, Logistic Regression etc • Thorough understanding of segmentation techniques CHAID, CART etc. • Good understanding of PD, EAD and LGD model development & validation process. • Significant experience of statistical / analytics project work • Experience of working in a financial services environment or Modelling department of International Banks • Strong analytical skills. • Excellent communication and interpersonal skills. Qualifications • Degree or equivalent standard, with a high numeric content e.g. Mathematics, Statistics, Operational Research, Economics, Physical Sciences. • Graduate calibre, with formal project management skills/qualifications.

Qualifications

• • Hands-on experience processing and analyzing data by using tools such as Python, R etc.• Good applied statistics skills, such as distributions, statistical testing, regression etc.• Good understanding of model predictive model development (such as Credit risk models, Fraud models etc.).• A strong understanding of machine learning techniques and algorithms, such as Gradient Boost, KNN, etc.• Knowledge of different modelling frameworks like Linear Regression, Logistic Regression, MultipleRegression, LOGIT, PROBIT, time- series modelling, CHAID, CART etc.• Understanding of decisioning and portfolio management in banking and financial services would be addedadvantage