JPMorgan Chase is a leading global financial services firm with assets of $2.5 trillion and operations in more than 60 countries. The firm is a leader in investment banking, commercial banking, financial services for small business and consumers, financial transaction processing, asset management and private equity.
The Wholesale Credit Analytics and Solutions team (WCAS) provides excellent exposure to any candidate interested in Credit Risk: CCAR, DFAST, ICAAP, Basel RWA, and Economic Capital. The group is responsible for implementing key credit risk practices across Wholesale businesses, and ensuring consistency in methodologies within Wholesale Credit Risk. The team works across CIB, CB and AM, and is closely aligned with firm-wide partners including Reporting, Finance, Model Risk & Development, Technology and the Regulatory Capital Management Office. We seek candidates with strong skills in finance, analytics, problem-solving, and communication. WCAS work is regularly presented to the JPMorgan’s Board of Directors and Operating Committee, and is also leveraged for external constituents, including the Firm’s investors and regulators.
WCAS’ areas of responsibility include Traditional Credit Product (TCP) stress testing (CCAR/DFAST/ICAAP) and Basel RWA, developing the firm's authoritative wholesale credit risk parameter data set, reserve/allowance management, development and implementation of an economic credit capital model, the design and integration of credit and capital limits, risk grading methodology, and the provision of strategic advice and solutions to the originating businesses. WCAS fosters a dynamic analyst environment which encompasses specific analyst training and networking events. Generally, there are mobility opportunities for good performers within the group.
Lead a team of high performing individuals responsible for Stress Testing analytics. The lead is required to facilitate the design and implementation of robust launch point and results analytics for all wholesale lines of business. Next gen analytics will be performed using Python, R, Tableau, SAS and other statistical software.
Analytics include but not limited to:
Launch point portfolio analysis to understand changes in portfolio quality
Variance analysis by building out walks across scenarios, across time, and across LOBs
Temporal analysis to illustrate how balances, losses, GII/NII and RWA change over the forecasted periods
Heat maps to visually display loss attribution
Top level dashboard which includes key methodological / implementation changes quarter to quarter
Partner with external vendors to develop strategic solutions to store stress testing data and automate results in an efficient manner
Oversee the enhancement of existing models related to the Held- For-sale/Fair Value Option (HFS/FVO) loan portfolio for CCAR/DFAST, Mid-Year ICAAP, and Risk Appetite
Partner with the lines of business and technology groups to test, implement and deploy strategic solutions in order to automate exposures related to Fronted Letters of Credit (LCs) and Swinglines as well as Pending Commitments within the TCP Portfolio
Provide and present analytic results, insights and recommendations to senior management across all lines of business
Ensure timely execution of monthly and quarterly processes to calculate fronted LC and Swingline exposures across the Wholesale business
Identify situations where data is insufficient, and develop creative workarounds or other means to synthesize reliable data sources to support analytic function
Investigate and build relationships with key stakeholders to resolve data quality issues for existing and new special projects
8+ years’ experience with a financial institution; Master degree or CFA a plus.
Familiarity with Basel requirements for credit risk/loss models.
Graduate level analytical and problem solving skills with the ability to interpret complicated and large amounts of data with business insights to support business growth.
Ability to build rapport and influence change.
Excellent written and verbal communication skills.
Self-motivated, organized and possess a high level of attention to detail.
Ability to work in a team environment to solve problems and excel in a high-pressure, deadline oriented environment.
Experience in econometric and statistical modeling for loss and interest income forecasting, risk management, or volume forecasting is highly preferred.
Experience with wholesale loans and commitments product and systems, as well as the Credit Risk Infrastructure / Credit Mart (CRI) and its data flows, is a plus.
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