This role pays less than
94%
of similar roles. Most pay
$150,000–$219,343
— the shaded band above.
At the midpoint, this role pays about
$118k
versus about
$185k
for comparable roles.
Based on 240 similar postings.
Employer
About MSCI
MSCI is a US-based provider of investment decision support tools, including equity indexes, portfolio risk and performance analytics, and ESG research used by institutional investors worldwide.
MSCI currently has
13 open roles
on FindRole.
Listed pay typically runs
$125,000–$187,000
across 13 roles with salary data.
Risk and Resilience Engineer
As a member of the Science team, you will work with scientists, engineers, and data experts to build sophisticated loss and resilience models that translate physical climate hazards into real-world financial impacts for buildings and infrastructure. You will develop models quantifying structural damage, repair costs, downtime, and economic consequences from perils like flood, wildfire, and wind. This interdisciplinary role requires expertise in engineering, materials science, cost estimation, statistics, and data science to turn imperfect datasets into actionable tools. You will utilize Python, machine learning, and version control systems like Git to develop scalable models while performing uncertainty quantification and validation. The work focuses on the critical intersection of physical climate risk and financial consequences, specifically modeling how investments in resilience and adaptation can mitigate damage across diverse geographies and various asset types.
Develop custom loss models to translate physical climate hazards into quantifiable financial impacts for buildings and infrastructure.
Estimate structural damage, repair costs, downtime, and economic impacts using engineering principles and statistical methods.
Transform imperfect or inconsistent historical datasets into technically sound, actionable modeling approaches.
Validate model performance by conducting rigorous statistical analysis of predictions, sensitivities, and uncertainty.
Translate academic research and industry standards into scalable, automated quantitative loss and resilience models.
Analyze building codes, construction materials, and regional practices to characterize the global built environment.
Model property-level adaptation scenarios to quantify the return on investment for resilience interventions.
What we're looking for
A Ph.D. is preferred; otherwise, a Master's degree with 3-5+ years of experience in structural engineering, civil engineering, operations research, resilience engineering, or catastrophe risk is required.
A degree in Structural Engineering or Civil Engineering (with a structural focus) is required.
Strong technical foundation in vulnerability and loss modeling, statistics, probabilistic methods, and quantitative analysis is required.
Hands-on experience developing risk, vulnerability, or loss models for buildings and infrastructure using engineering, statistical, or machine learning techniques is required.
Experience developing scalable catastrophe risk models across large portfolios, diverse asset types, and multiple geographic regions is required.
Advanced proficiency in Python or comparable scientific programming languages with experience in version control systems like Git is required.
Experience applying machine learning and AI techniques to engineering, risk, resilience, or other scientific modeling problems is required.
A record of scientific research and publication demonstrating the ability to communicate complex methodologies clearly is required.
SQL
Python
R
SAS
Oracle
AWS
Azure
GCP
Apache Airflow
Tableau
Qlik Sense
CI/CD
Machine Learning
Data Engineering
Statistical Modeling
Time-Series Forecasting
Data Visualization
Risk Management Framework
ACAS
STIGing
eMASS
Network Security
System Administration
Windows
Linux
DevSecOps
Firewalls
Intrusion Prevention Systems
Configuration Management
Data Flow Diagrams
Boundary Diagrams
Big Data Analytics