The successful candidate will be a key member of the Core Analytics Implementation and Execution team within Wholesale Credit Quantitative Research. The team focuses on the design, implementation, delivery and support of models for the firms Wholesale Credit Stress (CCAR, ICAAP, Risk Appetite) and Loan loss reserves (CECL, IFRS9) models. The candidate will implement and evolve state of the art risk valuation models in Wholesale Credit; explain the forecasted results to the lines of business and resolve issues based on business feedback; communicate model related information such as risk, performance and results to senior management and business partners; participate in enhancing and developing our strategy modelling frameworks for the next generation of Wholesale Credit forecasting, valuation and econometric models.
Work on the design and implementation state of the art forecast and valuation models in Wholesale Credit. Work closely with the modelling team and the business to gather and understand requirements for this. Contribute to the development of wrapper models. Will be responsible for advancing the methodology as well as the underlying model frameworks and implementation in the teams Python analytics library.
Explain the results to the lines of business and resolve issues based on business feedback in a timely fashion. Communicate model related information such as risk, performance and results to senior management and business partners. Document implementation, testing and results explanations.
This position will require the candidate to work with other experienced modellers, business and technology partners to achieve business goals and deliverables.
Ph.D or MS in a numerate subject (e.g. Applied Math, Physics, Computational Biology, Engineering, Math Finance, etc)
Strong quantitative problem solving skills and experience with application of numerical techniques to modelling. Ability to understand quantitative finance model specifications and translate into practical solutions and software implementation.
Excellent quantitative programming skills in Python; C++ a plus
Focus on functional and numerical testing through entire model development software cycle
Must be self-motivated, pro-active, responsible and driven to deliver
Experience implementing, integrating and deploying financial models end-to-end
Experience with Monte-Carlo, Quantitative finance
Experience with Credit Risk modeling in either Wholesale or Retail (PD/LGD/EAD)
Knowledge of CCAR/Stress, Allowance methodology (IFRS 9/CECL), Basel II and III regulatory capital in Wholesale Credit and/or Commercial real estate.
Proven ability to develop collaborative relationships with key internal partners to achieve objectives and prioritizations
Proficient working in a Linux/UNIX environment
JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.