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Analytics & Modeling Analyst (Inland Flood)

Pay
Pay not listed
Work setting
Not stated
Employment
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What you’ll work on

Full posting
  • Support development and validation of inland flood catastrophe models

  • Calibrate and validate model components against observed data and benchmarks

  • Analyze flood footprints, hazard metrics and loss patterns

  • Prototype AI and machine learning methods for flood modeling tasks

  • Maintain reproducible scientific workflows and data pipelines

From the employer’s posting
Support the development and validation of inland flood catastrophe models
Calibrate and validate model components using observed datasets, benchmark studies, and published research
Analyze flood footprints, hazard metrics, and loss patterns using statistical methods, geospatial tools, and data visualization techniques
Explore, prototype, and assess artificial intelligence and machine learning methods
Develop and maintain reproducible scientific workflows, data processing pipelines, and code repositories
Education & alternatives
Education - PhD in Hydrology, Hydraulics, Coastal and Ocean Engineering, Climate Science, Applied Mathematics, Data Science, Computer Science or a related quantitative discipline - Candidates with a relevant MSc and strong research or industry experience are also encouraged to apply
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Status in our records
Live
First seen by us
Oct 20, 202625 days ago
Recorded sightings
64
Last seen by us
Nov 13, 2026

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How this job compares

Time since we first saw it

Day 25

Similar jobs usually come down between day 8 and day 76.

At Moody's, jobs usually come down by about day 15.

From 5,460 similar postings that came down, and 78 of Moody's’s. Coming down is not always a hire.

Moody's is hiring

54 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Takes jobs down at a usual pace
  • 11% of open jobs were reposted
Hiring record

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  • Bash
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  • Python
  • PyTorch
  • SQL
Where the posting names them
- Experience with one or more machine learning or deep learning approaches such as Long Short-Term Memory networks, Convolutional Neural Networks, and frameworks including PyTorch - Practical programming experience in Python and familiarity with scientific computing environments; experience with R, C/C++, Fortran, CUDA, or Bash is preferred - Ability to analyze, validate, and interpret large geospatial, hydrological, and climate datasets using statistical methods and visualization techniques
- Understanding of machine learning fundamentals including model training and validation, overfitting, and model evaluation - Experience with one or more machine learning or deep learning approaches such as Long Short-Term Memory networks, Convolutional Neural Networks, and frameworks including PyTorch - Practical programming experience in Python and familiarity with scientific computing environments; experience with R, C/C++, Fortran, CUDA, or Bash is preferred
- Strong communication and collaboration skills with the ability to explain technical concepts to multidisciplinary stakeholders - Experience working with high-performance computing environments, geographic information systems, structured query language, or data assimilation techniques is desired - Basic understanding of artificial intelligence concepts, with curiosity and enthusiasm for learning how AI tools can be used to improve processes and drive efficiency

Your next step

Complete your application on careers.moodys.com. The employer’s form will show what is required.

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About Moody's

People
About 5,300 in 2025, up 10% from the year before.Annual report (SEC Form 10-K)
Company
Listed as MCO.

Demand for ML Engineer roles

New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.

Weekly new ML Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 612, the most 1,081, and the latest week had 1,068.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.

If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity. 


Skills and Competencies

  • Strong foundation in hydrology, hydraulics, climate science, applied mathematics, data science, computer science, or a related quantitative discipline
  • Understanding of machine learning fundamentals including model training and validation, overfitting, and model evaluation
  • Experience with one or more machine learning or deep learning approaches such as Long Short-Term Memory networks, Convolutional Neural Networks, and frameworks including PyTorch
  • Practical programming experience in Python and familiarity with scientific computing environments; experience with R, C/C++, Fortran, CUDA, or Bash is preferred
  • Ability to analyze, validate, and interpret large geospatial, hydrological, and climate datasets using statistical methods and visualization techniques
  • Strong communication and collaboration skills with the ability to explain technical concepts to multidisciplinary stakeholders
  • Experience working with high-performance computing environments, geographic information systems, structured query language, or data assimilation techniques is desired
  • Basic understanding of artificial intelligence concepts, with curiosity and enthusiasm for learning how AI tools can be used to improve processes and drive efficiency
  • Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use

Education

  • PhD in Hydrology, Hydraulics, Coastal and Ocean Engineering, Climate Science, Applied Mathematics, Data Science, Computer Science or a related quantitative discipline 
  • Candidates with a relevant MSc and strong research or industry experience are also encouraged to apply

Responsibilities

Support the development and validation of inland flood catastrophe models used to quantify and manage flood risk globally.

  • Contribute to the development and application of large-scale flood hazard model components including stochastic rainfall generation, rainfall-runoff processes, hydraulic routing, floodplain inundation, and flood defense failure mechanisms
  • Calibrate and validate model components using observed datasets, benchmark studies, and published research to improve model performance and reliability
  • Analyze flood footprints, hazard metrics, and loss patterns using statistical methods, geospatial tools, and data visualization techniques
  • Research, evaluate, and implement innovative scientific and computational approaches to advance flood hazard modeling capabilities
  • Explore, prototype, and assess artificial intelligence and machine learning methods for surrogate modeling, spatial downscaling, anomaly detection, feature extraction, emulator development, and workflow automation
  • Develop and maintain reproducible scientific workflows, data processing pipelines, and code repositories using modern software development practices
  • Collaborate with scientists, engineers, and data specialists to support model development, quality assurance, and scientific innovation
  • Communicate research findings and modeling insights to internal stakeholders, clients, and the broader scientific community through presentations, reports, and technical discussions

About the Team

Our Model Development team is responsible for building industry-leading natural catastrophe models that help quantify the financial impacts of natural hazards. The team brings together scientists, engineers, and data specialists working across flood, storm, and climate-related perils to advance risk analytics through cutting-edge research, large-scale modeling, and data innovation. By joining the Inland Flood team, you will contribute to high-impact scientific work in a collaborative environment that embraces the responsible use of artificial intelligence, machine learning, and advanced computational techniques to improve model performance and decision-making.


Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.

Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.

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London, England

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