Vice President - Mortgage Servicing - Credit Risk Analytics - Loss Recognition - Hybrid

Posted 3 days ago

citigroupMissouri (MO)

SENIORITY

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About the role

Vice President - Mortgage Servicing - Credit Risk Analytics - Loss Recognition - Hybrid Discover your future at Citi Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you'll have the opportunity to grow your career, give back to your community and make a real impact.
Job Overview: The VP, Residential Real Estate Risk – Risk Analytics -Loss Recognition is responsible for risk analytics, controls and monitoring of mortgage loss recognition activities (FFIEC) for the US residential real estate portfolio. In this role, you will develop and maintain robust controls and monitoring routines for risk oversight of loss recognition activities in compliance with Citi Retail Credit Policy and applicable supervisory guidelines. You will work closely with independent risk management and policy team and stakeholders in mortgage-servicing, finance, controls, supporting both US Personal Banking and Wealth divisions on an expansive set of portfolio objectives to deliver performance aligned with Citi's risk-appetite framework.
Responsibilities: Use sophisticated analytical techniques to monitor execution of loan loss recognition activities in compliance with Citi policy requirements and applicable supervisory guidelines. Monitor credit loss performance trends, understand drivers to incremental credit loss and identify loss mitigation opportunities through the loan life cycle. Understand the write-down process. Conduct root cause analyses of any concerns from auditors or internal partners and provide recommendations to address concerns. Develop, track and report on key initiatives, performance results, and emerging trends, analyzing risks, and ensure appropriate escalation and communication is provided to senior leadership. Utilize analytical tools such as SAS, R, SQL or Python (SAS preferred), to develop data driven insights with regards to loss recognition activities. Manage the audit and control environment, end-user computing portals, audit self-assessments, and interact with internal/external auditors. Understand relevant supervisory guidelines, FFIEC guidelines, accounting and credit policy requirements applicable to write-down, write-off, charge-off, loan impairment and loss recognition for a supervised depository institution. Establish cross-functional partnerships and networks in order to develop and implement loss-mitigation strategy and support the execution of cross-functional business compliance with applicable laws, rules and regulations, adhere to Policy, apply sound ethical judgment regarding personal behavior, conduct and business practices, and escalate, manage and report control issues with transparency, as well as effectively supervise the activity of others and create accountability with those who fail to maintain these standards.
Qualifications: 8-12 years of related analytic experience using quantitative analysis, in a consumer credit management or finance/accounting for loss recognition in a supervised bank context is preferred. Experience in the residential real estate (mortgage/home-equity) industry highly preferred. Proficiency in writing SAS codes with macro, or proficiency in other data management tools, such as R, SQL or Python (can be trained to use SAS quickly). Experience with SQL programming in a UNIX environment is preferred. Demonstrated ability to synthesize, prioritize and drive results with a high sense of urgency. The successful candidate will have demonstrable analytic, interpersonal and project management skills. Ability to work effectively on virtual teams, including across different geographies and time zones preferred. Consistently demonstrate clear and concise written and verbal solid cross-functional partnerships and networks to contribute and execute cross-functional and business 's degree/University degree. Major in Finance/Accounting or quantitative fields such Statistics, Mathematics, or Engineering preferred. Advanced degrees considered a plus.

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