Physical Climate Risk Model Developer, Vice President

Overview

Develop quantitative models and methodologies to assess physical climate and nature-related risks across Citi's portfolios and operations. Support risk identification, scenario analysis, stress testing, and risk management using quantitative modeling, geospatial analytics, and data science.

What you'll do

• Develop and implement quantitative models assessing physical climate and nature-related risks, including hazard, exposure, vulnerability, and financial/credit impacts across portfolios, sectors, and geographies.
• Build geospatial analytical pipelines to evaluate acute and chronic physical hazards using complex climate, environmental, and asset-level datasets.
• Translate scientific, environmental, and physical risk drivers into financial metrics for stress testing, scenario analysis, and portfolio analytics.
• Leverage big data and machine learning techniques to solve complex data science challenges and deliver deployable analytical solutions.
• Evaluate and integrate external climate, hazard, geospatial, and nature-related datasets/models with internal risk and portfolio data.
• Partner with external vendors and internal First Line (1LOD) and Second Line of Defense (2LOD) on sourcing hazard, exposure, and vulnerability data and designing climate risk assessment features.
• Write production-grade analytical code and support model implementation in controlled technology environments.
• Perform model testing, sensitivity analysis, benchmarking, and quantitative assessments to evaluate model performance and limitations.
• Author technical documentation for model governance, validation (MRM), and regulatory requirements.
• Collaborate across Risk, Technology, Model Risk Management, and business sponsors.
• Present analytical findings and model performance clearly to technical and non-technical audiences.

What you'll need

• 6–10+ years of relevant experience in quantitative modeling, data science, climate risk, catastrophe (CAT) modeling, or environmental risk analytics.
• Advanced degree (MS or PhD) in a quantitative discipline: Hydrology/Meteorology, Climate Science, Environmental or Life Sciences, Statistics, Physics, Engineering, Mathematics, or other natural hazard related disciplines.
• In-depth knowledge of industry and academic climate and CAT modeling methodologies, vendor/public weather and hazard models, and climate data sources.
• Proven expertise in physical climate risk, natural hazard modeling, nature risk, or geospatial analytics.
• Professional experience in statistical modeling, Bayesian inference, spatial statistics, or machine learning methodologies.
• Expertise in programming, data modeling, and data management.
• Strong proficiency in Python, SQL, and Unix/Linux environments.
• Strong technical documentation skills with proven experience preparing quantitative methodologies for model validation and governance.
• Excellent communication and presentation skills, with demonstrated ability to translate complex model design, assumptions, and performance metrics to technical and non-technical stakeholders.

Nice to have

• Bachelor's/University degree.
• Master’s degree.

Details

• Location: Mumbai, Maharashtra, India.
• Full time.