Risk and Resilience Engineer

MSCI · New York City

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We are looking for an expert in risk and resilience of the built environment to help advance how physical climate risk is translated into real-world financial impacts.

Our Science teams develop world-class hazard models across a range of natural perils. In this role, you will work closely with scientists, engineers, and data experts to build sophisticated loss and resilience models that quantify what those hazards mean for buildings and infrastructure around the world.

You will develop models that translate exposure to hazards such as flood, wildfire, and wind into estimates of structural damage, repair costs, downtime, and broader economic impacts. These models are a critical link between physical and financial risk, enabling our customers to understand not only where risk exists, but what it could mean financially—and how investments in resilience and adaptation can reduce that risk.

This is a highly interdisciplinary role at the intersection of engineering, resilience, materials science, cost estimation, statistics, and data science. We are particularly interested in candidates who are comfortable solving complex technical problems where data may be incomplete, inconsistent, or scarce, and who can combine first-principles reasoning with empirical evidence and modern quantitative methods to develop defensible, scalable solutions.

• 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.

The ideal candidate brings expertise across several disciplines rather than fitting neatly into a single technical category. Your background may include structural or civil engineering, resilience engineering, materials science, catastrophe or risk modeling, construction cost estimation, statistics, or data science.

You are comfortable moving between engineering fundamentals and large datasets, challenging assumptions, working through uncertainty, and developing practical solutions when perfect data does not exist. Most importantly, you are excited by the opportunity to build models that transform complex climate science into information that can support better financial, infrastructure, and resilience decisions.

This is an opportunity to work on technically challenging problems with meaningful real-world impact—helping define how climate-driven physical risk, financial consequences, and the value of resilience are understood at the property level and at global scale.

• 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.
• Structural Degree or Civil Engineering (with a structural focus).
• 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.

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.

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