Credit Risk

· Develop and validate Credit Risk models

· Using SAS, R, Python for model building and model validation

· Continual enhancement of statistical techniques and their applications in solving business objectives

· Compile and analyze the results from modeling output and translate into actionable insights

· Prepare PowerPoint presentations and document preparation for the entire Credit Risk modeling process

· Collaborate, Support, Advise and Guide in development of the models

· Acquire and share deep knowledge of data utilized by the team and its business partners

· Participate in global conference calls and meetings as needed and manage multiple customer interfaces

· Execute analytics special studies and ad hoc analyses

· Evaluate new tools and technologies to improve analytical processes

· Set own priorities and timelines to accomplish projects (accountability for project deliverables)

Qualifications for Internal Candidates
Skills/Knowledge required

· Masters in Finance, Financial Engineering, Analytics or Mathematics, Computer Science, Statistics, Industrial Engineering, Operations research, or related field.

· Good understanding of Probability of Default (PD), LGD and EAD modeling technique.

· Very good understanding of Predictive modeling techniques and their application.

· Knowledge of Credit life cycle

· Statistics and machine learning techniques.

· Conducted and applied statistical methodologies including linear regression, logistic regression, ANOVA/ANCOVA, CHAID/CART, cluster analysis

· Team player and collaboration skills.

· Programming skills in R, SAS, and PYTHON.

· Fluency with Excel, PowerPoint and Word

· Strong written and oral presentation / communication skills – must have the ability to convey complex information simply and clearly

· Experience with developing and implementing cloud based analytical solutions in GCP or similar set up.

Qualifications

· Ph.D. or Masters in Mathematics/Statistics/Economics/Engineering or any other related discipline or a track record of performance that demonstrate this ability

· Practical applications of mathematical modeling, Operations Research and Machine Learning techniques

· Good exposure to ML techniques such as Clustering/classification/decision trees, Random forests, Support vector machines, Deep Learning, Neural networks, Reinforcement learning, and related algorithms

· Demonstrated knowledge in credit and/or market risk measurement and management

· Excellent problem solving, communication, and data presentation skills

· Proficient with SAS, SQL

· Familiarity with any of R, Python, Alteryx, GCP suite.

· Experience with any of Qlikview, Tableau

Experience:

· 3 - 5 Years exposure in Banking & Financial Services industry

· Candidate should have worked in Credit Analytics (Mandatory) and preferably in Financial Analytics, Retail bank, Mortgage, Lending / liability product

· Risk Analytics, Credit Risk Scorecard Development, Model Validation, IFRS 9 Validations, Credit Loss Forecasting