The Risk Analytics Officer will be responsible for developing and executing the Credit Risk Loss Forecasting models for the Consumer Bank retail portfolios. The candidate will utilize the analytical framework and help management in ensuring accurate loss forecasts are developed and communicated to the various stakeholders across the bank. The candidate will also analyze historical data and apply integrative thinking to identify trends and make business recommendations. The candidate must be able to work efficiently in a high volume, fast paced, and deadline/results-oriented environment.
MAIN RESPONSIBILITIES:
- Supporting management activities around loss forecasting group as it relates to predicting risk factors such as losses, delinquencies, prepays and other related risk events.
- Help management in developing/maintaining existing forecast models such as roll-rate models, which can accurately predict losses in near term, and aspire to get reasonable accuracy for long term forecast by also leveraging Expected Loss models as well as macro-economic loss models in addition to the roll-rate models.
- Seeks opportunities to improve forecasting platform, methods, and techniques to produce more accurate forecasts and improve processes to meet management needs.
- Help develop analytics for backtesting, model diagnosis, and error attribution.
- Generate hypotheses, analyze data to prove hypothesis and make recommendations for improvement.
- Help management in running the monthly as well as long term production loss forecasting cycles and tracking the actuals against prior forecasts.
- Help management in running the annual and quarterly CCAR production runs leveraging the macro-economic loss models.
- Develop/deliver regular management reports and analyze business problems.
- Help implement strategies in conjunction/collaboration with other business partners/teams.
Required Qualifications:
- 2-4 years of financial or quantitative experience.
- 2+ years of experience programming in SAS, SQL, MS-Word, Power Point, and spreadsheets.
- Bachelor's degree in a quantitative disciple (e.g., Mathematics, Statistics, Engineering, Physics, Computer Science, Economics).
- Strong experience in R and Phyton
- Creative analytic problem-solving skills.
- Good written and presentation abilities.
