Climate Risk & Resilience Modeler

MSCI Inc.; New York (NY); On-site; USD 102,000 - 133,000; Full time

Benefits offered by this job

Salary range

Annual bonus

Flexible working arrangements

Learning platform access

Job summary

MSCI Inc invites an experienced Risk & Resilience Modeler to join our Science team. You will translate physical climate risk into financial impacts for buildings and infrastructure worldwide, developing loss and resilience models that quantify hazards and costs.

Collaborating with scientists, engineers, and data professionals, you will build scalable models using first-principles reasoning, statistics, and machine learning, while ensuring transparency, validation, and reproducibility. A Ph.D.

Qualifications

• Ph.D. preferred, or a Master’s degree with 3 - 5+ years of relevant experience in structural engineering, civil engineering, operations research, resilience engineering, catastrophe risk, or a related quantitative field.
• Strong technical foundation in vulnerability and loss modeling, statistics, probabilistic methods, and quantitative analysis, with the ability to translate complex physical processes into robust analytical models.
• Hands-on experience developing risk, vulnerability, or loss models for buildings and infrastructure, using engineering-based approaches, statistical methods, machine learning, or a combination of these techniques.
• Demonstrated ability to develop loss models from the ground up, from defining the underlying methodology and sourcing appropriate data through calibration, validation, uncertainty quantification, and implementation.
• Experience working with multi-hazard and catastrophe risk data, including hazard intensity measures, building-level damage and loss observations, exposure datasets, construction characteristics, and repair or construction cost data.
• Experience developing scalable and generalizable catastrophe risk models that can be applied across large portfolios, diverse asset types, and multiple geographic regions.
• Advanced proficiency in Python or comparable scientific programming languages, with experience developing reliable, maintainable, and reproducible analytical workflows.
• A rigorous, science-driven approach to model development, with a strong emphasis on accuracy, reliability, transparency, validation, and reproducibility.
• Strong understanding of the current research landscape in resilience, vulnerability, catastrophe risk, and loss modeling, including relevant engineering standards, technical guidelines, and emerging methodologies.
• Experience applying machine learning and AI techniques to engineering, risk, resilience, or other scientific modeling problems.
• Experience analyzing large and complex datasets in high-performance computing environments, either on-premises or using cloud platforms such as AWS, GCP, or Azure.
• Proficiency with collaborative software development practices and version control systems such as Git.
• A strong record of scientific research and publication, demonstrating the ability to conduct rigorous technical work and communicate complex methodologies and findings clearly.
• What Will Make You Stand Out: We are especially interested in candidates who combine deep technical expertise with the ability to operate in areas where established datasets or methodologies may not yet exist. You know how to move from first principles to a working model, make thoughtful assumptions when evidence is limited, test those assumptions against available data, and communicate uncertainty clearly.
• Experience building property-level loss models from scratch, working across multiple natural hazards, or developing models that connect physical damage and recovery to financial outcomes will be particularly valuable.

Responsibilities

• Connect physical climate risk to financial risk by developing custom loss models that quantify impacts to buildings, infrastructure, and other assets globally, integrating them with First Street’s hazard models.
• Model damage, recovery, and economic impacts by developing estimates of structural damage, repair costs and timelines, downtime, and indirect impacts using approaches including engineering first principles, cost estimation, statistical methods, and machine learning.
• Turn imperfect data into actionable models by analyzing historical loss and observational datasets, identifying data limitations and quality-control issues, and developing technically sound approaches to address them.
• Validate model performance and quantify uncertainty through statistical analysis of model predictions, observational data, sensitivities, and uncertainty to ensure outputs are scientifically rigorous and decision-useful.
• Translate research into scalable modeling approaches by evaluating academic literature, engineering research, industry standards, and emerging methodologies and incorporating relevant insights into quantitative loss and resilience models.
• Characterize the global built environment by analyzing building codes, exposure datasets, construction practices, materials, occupancy types, and regional differences to inform vulnerability and loss model development across diverse geographies.
• Model the value of resilience and adaptation by developing property-level adaptation scenarios that allow customers to understand how protective measures can reduce damage and downtime and evaluate the potential return on investment of resilience interventions.

Skills

Risk modeling

Resilience modeling

Catastrophe risk

Statistics

Data science

Machine learning

Python

HPC / cloud computing

Scientific research & publications

Education

Ph.D. preferred

Master’s degree + 3-5+ years in structural civil engineering or related quantitative field

Tools

Python

Job description

MSCI Inc invites an experienced Risk & Resilience Modeler to join our Science team. You will translate physical climate risk into financial impacts for buildings and infrastructure worldwide, developing loss and resilience models that quantify hazards and costs.

Collaborating with scientists, engineers, and data professionals, you will build scalable models using first-principles reasoning, statistics, and machine learning, while ensuring transparency, validation, and reproducibility. A Ph.D.