• 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

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,Python

Experience with any of Power BI 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

 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