Risk Management

2 weeks ago


Columbus, United States JPMorgan Chase & Co. Full time

Bring your expertise to JPMorgan Chase. As a part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class.

The firm relies on lines of business and functions to identify and document material risks, and to manage, control, monitor, and escalate risks as appropriate. To support this objective, the firm has implemented a Risk Identification (Risk ID) framework for capturing existing and topical risks inherent in the firm’s activities and the global business environment.

The Risk Identification Analytics Lead will join the highly visible Firmwide Risk ID team that is responsible for maintaining this framework. Specifically, the successful candidate will serve as the team's Analytics Lead, responsible for developing statistical and automation capabilities that support management of the firm’s Material Risk Inventory (MRI), senior management’s review and prioritization of risks and strengthening of linkages with other processes in the firm.

Job Responsibilities:

Developing automated capabilities (., targeted reporting, diagnostics) that support core Risk ID objectives (., maintaining data quality of the MRI and integrity of quarterly processes) Identifying, scoping and developing strategic statistical capabilities that help develop a forward-looking view of the firm's risk profile Conducting analysis leveraging the MRI and other sources of data to distill high-level themes  Developing audience-appropriate written communications and reports

Required qualifications, capabilities and skills:

Minimum Bachelor’s degree in a quantitative or technical field (., statistics, mathematics, quantitative economics, computer science) Core proficiency in developing and implementing Large Language Models (LLMs)/Machine Learning (ML) driven capabilities; additional expertise in traditional econometrics or statistics is highly preferred  Comfort with ambiguity and problem-solving mindset - specifically the ability to bridge business objectives and required technical approach Ability to leverage relevant components in the LLM ecosystem including relevant languages (., Python) and accompanying toolsets (., Opensource models and repositories)

Preferred qualifications, capabilities and skills:

Expertise in traditional econometrics or statistics is highly preferred 
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