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Quantitative Modeler
Mitsubishi UFJ Financial Group
Monterey Park, CA, United States
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Description
Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), the 5th largest financial group in the world (as ranked by S&P Global, April 2018) with total assets of over $2.9 trillion (106.2 (JPY) as of March 30, 2018) and 150,000 colleagues in more than 50 countries. In the U.S., we’re 13,000 strong, working together to positively impact every customer, organization, and community we serve. We achieve this by delivering on our values, putting people first, fostering long-term relationships built on honesty and mutual understanding, and inspiring the best in each other. This is all part of our inclusive, high-performing culture supported by Total Rewards that include our cash balance pension plan. Join a team that’s working to fulfill its vision to be the world’s most trusted financial group.
Job Summary:
Reporting to the Operational Risk Workstream Lead, the Operational Risk Quantitative Modeler will work on statistical modeling aspects with a team that is responsible for the construction, testing, and implementation of the bank's Operational Risk (OpRisk) stress models to forecast loss estimates from various macroeconomic predictors. The modeler will play a major role in building new models, writing all related documentation, conducting various analyses of model results, testing controls of the modeling process flow, and running any/all stress models from start to finish on a semi-annual basis. Also participate in ideation activities with other members of the modeling team to improve existing models and address modeling deficiencies.
Major Responsibilities:
• Build/Review regression models, probabilistic models, conduct correlational analysis, and execute/interpret multicollinearity tests.
• Run all components of the bank's stress models on a periodic basis.
• Coordinate project efforts with the modeling team to meet deadlines and remediate modeling limitations.
• Work with the OpRisk Workstream Lead and other modelers to recommend viable statistical approaches for addressing model limitations and effective approaches for sensitivity analysis.
• Build non-additive models by the use of interaction predictor variables and develop effective heteroscedasticity detection and correction schemes
• Write/maintain model documentation.
Qualifications
• Requires a graduate degree in Actuarial Science, Economics, Finance, Mathematics, or Statistics (Ph.D. preferred) – emphasis on applied regression analysis is preferred.
• Experience in building complex multiple regression models
• Experience within the financial services industry is a plus (not required).
• Financial risk management industry certifications a plus.
• Should be well-versed and experienced in applied multiple regression analysis
• A working knowledge of the role of capital risk-based ratios in stress forecasting is a plus (not required)
• Should also have expertise in building or interpreting Non-parametric models, and Principal components analysis models.
• Must have strong data management skills
• Should have proficiency in each of the following packages/languages:
• Base SAS (should be an advanced user)
• Excel (should be an advanced user)
• R (advanced user a plus, but not required)
• SAS/STAT (advanced user a plus, but not required)
• Good verbal and written communication skills.
The above statements are intended to describe the general nature and level of the work being performed. They are not intended to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified.
We are proud to be an Equal Opportunity / Affirmative Action Employer and committed to leveraging the diverse backgrounds, perspectives, and experience of our workforce to create opportunities for our colleagues and our business. We do not discriminate in employment decisions on the basis of any protected category.
A conviction is not an absolute bar to employment. Factors such as the age of the offense, evidence of rehabilitation, seriousness of violation, and job relatedness are considered in all employment decisions. Additionally, it’s the bank’s policy to only inquire into a candidate’s criminal history after an offer has been made. Federal law prohibits banks from employing individuals who have been convicted of, or received a pretrial diversion for, certain offenses.