Transition Risk Modelling - Senior Associate in London

This is a great opportunity to become a part of Risk Management and Compliance, where you are at the centre 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.

As a Transition Risk Modelling - Senior Associate in the Climate Nature and Social (CNS) Risk Management team, you will be responsible for developing the firm’s approach to climate-related risk management, including both transition and physical risks. You will bring strong modelling, data, research and communication skills to support the build-out of climate risk capabilities across the firm. You will interact with a variety of internal counterparts across the firm, learning about the many aspects relevant in assessing the potential implications of climate change on a large and global financial institution. Your strong track record of ownership and accountability will be key to the success of this role, along with being able to work effectively in an agile and highly collaborative environment.

Job responsibilities

• Provide support to the team’s climate and transition risk analytical frameworks, with a focus on developing methodologies and conducting research on financial impacts of transition risk.
• Contribute to the design or enhancement of internal climate scenarios and expanded variables to meet internal stakeholders’ requirements, as well as interrogate and assess appropriate models from external parties, including vendors.
• Help build, maintain and analyse outputs from transition risk assessment models, scenario models and other user tools.
• Help address regulatory expectations and requirements for managing climate-related risk and produce documentation to support climate risk assessment models and user tools.
• Contribute content to the firm’s public and regulatory disclosures.
• Become an expert in the datasets we use to manage climate risk, forming conclusions on key trends and patterns and working with data providers to maximise our knowledge.
• Support central data and modelling teams to source and integrate climate risk data into our risk models.
• Support continuous improvement of the team’s delivery, as well as expansion into new analytics use cases, through an understanding of opportunities to leverage AI tools or automation processes.

Required qualifications, capabilities and skills

• Work experience in transition risk modelling, energy systems, energy and climate policy, climate science, or related fields as well as demonstrable work experience with climate scenarios, NGFS, IEA, IPCC SSP-RCPs, etc.
• Strong university degree (2:1 equivalent or above), in economics, geography, environmental science or energy systems.
• Strong technical understanding of transition risks including climate and energy policy, technology risks, and consumer preference changes and transmission channels within an economy.
• Excellent analytical skills: comfortable working with large datasets, knowledge of Python or R is a must.
• Strong sense of ownership and ability to work independently in producing deliverables with high standards and within tight deadlines.
• Ability to convey information clearly, accurately and succinctly (both written and verbally) and collaborate effectively with a range of different stakeholders across the firm.
• Ability to draft high-impact presentations and documentation, strong attention to detail.

Preferred qualifications, capabilities, and skills

• Experience in working on highly quantitative and analytical projects.
• Experience related to Operations Research (e.g., optimisation frameworks such as GAMS).
• Experience related to statistical analysis, in particular econometrics.
• Understanding of climate-related transition and physical impacts on macro-economic variables.
• Leverage awareness of AI tools and processes relevant to the development and application of analytical models.