Physical Climate Risk Model Developer, Vice President

Growth For Impact; Mumbai; On-site; INR 1,400,000 - 2,600,000; Full time

Job summary

Growth For Impact seeks a senior quantitative analyst to develop climate and nature-related risk models, translating environmental drivers into financial metrics for stress testing and portfolio analytics.

You will build geospatial pipelines, leverage big data and ML techniques, and integrate external climate datasets with internal risk data to deliver deployable solutions.

Qualifications

• Proven ability to build and validate quantitative models for climate risk and portfolio impact.
• Experience translating environmental drivers into financial metrics for stress tests and scenario analysis.
• Strong programming and data processing skills for large, complex datasets.

Responsibilities

• Develop and implement quantitative models assessing physical climate and nature-related risks across portfolios and geographies.
• Build geospatial analytical pipelines to evaluate hazards using climate, environmental, and asset data.
• Translate risk drivers into financial metrics for stress testing and portfolio analytics.
• Utilize big data and machine learning to deliver deployable analytical solutions.
• Integrate external climate datasets with internal risk data for enhanced modeling.
• Write production-grade analytical code and support model deployment.
• Perform model testing, sensitivity analyses, and benchmarking.

Skills

Quantitative modeling

Geospatial analysis

Machine learning

Data wrangling

Education

Master's/PhD in a quantitative field

Tools

Python

SQL

Spark

R

Job description

ROLE

• 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.
• 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