This role pays less than
90%
of similar roles. Most pay
$142,500–$215,000
— the shaded band above.
At the midpoint, this role pays about
$118k
versus about
$179k
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
15 open roles
on FindRole.
Listed pay typically runs
$195,000–$250,000
across 15 roles with salary data.
As a Risk and Resilience Engineer, you will join the Science team to bridge the gap between physical climate risk and real-world financial impacts for buildings and infrastructure. You will develop sophisticated loss and resilience models that translate hazards like flood, wildfire, and wind into estimates of structural damage, repair costs, and downtime. Your daily work involves translating research into scalable modeling approaches, characterizing the global built environment through building codes and construction practices, and quantifying the value of adaptation measures. To succeed, you must master a multidisciplinary toolkit including structural engineering, materials science, cost estimation, statistics, and data science. You will utilize Python, machine learning, and AI techniques to process large datasets in high-performance computing environments while using Git for version control. This role solves the critical problem of quantifying how physical risks translate into financial consequences.
Develop custom loss models to translate physical climate risks into quantifiable financial impacts for buildings and infrastructure.
Estimate structural damage, repair costs, downtime, and economic impacts using engineering principles and statistical methods.
Create robust modeling approaches by analyzing historical datasets and addressing issues related to imperfect or scarce data.
Validate model performance and quantify uncertainty through rigorous statistical analysis of predictions and observational data.
Translate academic research and industry standards into scalable quantitative models for loss and resilience.
Analyze building codes, construction materials, and regional differences to characterize the global built environment.
Model property-level adaptation scenarios to evaluate how protective measures reduce damage and improve return on investment.
What we're looking for
A Ph.D. is preferred; otherwise, a Master's degree with 3 to 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.
Candidates must have a strong technical foundation in vulnerability and loss modeling, statistics, probabilistic methods, and quantitative analysis.
Experience developing risk, vulnerability, or loss models for buildings and infrastructure using engineering-based approaches, statistical methods, or machine learning is required.
Experience developing scalable and generalizable catastrophe risk models across diverse asset types and geographic regions is required.
Advanced proficiency in Python or comparable scientific programming languages with experience in version control systems like Git is required.
Experience analyzing large, complex datasets in high-performance computing environments (on-premises or cloud platforms) is required.
A record of scientific research and publication demonstrating the ability to communicate complex methodologies clearly is required.
Reliability Engineering
System Safety
FMECA
MIL-STD-882E
Probabilistic Risk Assessment
Test Planning
Aerospace Engineering
Mechanical Engineering
Solid Rocket Motor Systems
RMF
ACAS
STIGing
eMASS
DevSecOps
Network Security
System Administration
Windows
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Firewalls
Intrusion Prevention Systems
Big Data Analytics
Configuration Management
Data Flow Diagrams
Boundary Diagrams
Systems Development Lifecycle
RMF
ACAS
STIG
eMASS
DevSecOps
Vulnerability Assessment
Network Security
Linux
Windows
Virtualization
Intrusion Prevention Systems
Firewalls
Big Data Analytics
Cybersecurity Policy
Systems Development Lifecycle