Analytics & Modeling Analyst - Flood Modeller

Moody's Analytics; Dadri, Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Moody's Analytics in India (Dadri area) invites experienced researchers to advance hydrologic and hydraulic flood models using Python, ML, and geospatial data. You will work on elevation data, GIS inputs, and model calibration with observed datasets.

The role emphasizes collaboration across multidisciplinary teams, development of scalable workflows, and applying AI methods to improve hazard assessment. A MSc in a quantitative field is required, with a focus on GIS/data science.

Qualifications

• Hands-on Python experience with scientific computing environments.
• ML model training and validation; understanding overfitting and model evaluation.
• Interest in ML, flood modelling, environmental systems, geospatial analytics.
• Familiarity with elevation models, LiDAR data cleaning, artefact removal.
• Experience with HPC Linux environments for large-scale model development.
• Experience with geospatial foundation models is advantageous.
• Ability to communicate technical concepts clearly within multidisciplinary teams.
• Experience using AI tools to streamline workflows and improve efficiency with responsible AI practices.

Responsibilities

• Join a collaborative scientific team building high-resolution, large-scale hydrologic and hydraulic models.
• Contribute to development of flood hazard model components and geospatial input layers.
• Calibrate and validate model components using GIS, observed data, and benchmark datasets.
• Evaluate model outputs such as flood footprints and loss patterns using statistics and GIS.
• Advance flood model input layers including elevation models and remotely sensed data.
• Prototype and evaluate AI/ML methods for model-development challenges.
• Contribute to reproducible scientific workflows, code repositories, and data pipelines.
• Communicate research findings to internal stakeholders and clients.
• Foster an inclusive culture through knowledge sharing and continuous learning.

Skills

Python programming

ML fundamentals

Geospatial analytics

HPC Linux

AI concepts

Communication

Team collaboration

Deep learning (LSTMs/CNNs)

Education

MSc in GIS, Hydrology, Hydraulics, Coastal/Ocean Engineering, Climate Science, Data Science, CS

Tools

PyTorch

GIS Software

Job description

Experience level: Experienced Hire

Job category: Data, Analytics Research

Job sub category: Data, Analytics Research

Line of business: Insurance

Skills and Competencies

• Practical programming experience in Python; familiarity with scientific computing environments, and experience with R, C/C++, Fortran, CUDA, or bash is advantageous
• Understanding of machine learning fundamentals, including model training and validation, overfitting, and model evaluation; experience with one or more deep learning models such as LSTMs or CNNs and frameworks such as PyTorch
• Demonstrated interest in ML, flood modelling, environmental systems, geospatial analytics, or related scientific applications
• Knowledge of elevation model refining strategies, such as cleaning of LiDAR data and artefact removal, is advantageous
• Familiarity with large-scale model development projects on HPC environments in Linux is advantageous
• Experience working with geospatial foundation models is advantageous
• Ability to communicate technical concepts clearly and collaborate effectively in multidisciplinary teams
• Demonstrated proficiency in artificial intelligence concepts, with hands‑on experience using AI tools to streamline workflows and enhance operational efficiency. Proven ability to implement AI-powered solutions to solve business challenges. Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use

Education

• MSc in GIS, Hydrology, Hydraulics, Coastal and Ocean Engineering, Climate Science, Applied Mathematics, Data Science, Computer Science, or a related quantitative discipline

Responsibilities

• Join a collaborative scientific team building high-resolution, large-scale hydrologic and hydraulic models and contribute to model components, data pipelines, validation, and research
• Contribute to the development and application of large-scale flood hazard model components, including elevation models and other geospatial input layers, stochastic rainfall, rainfall-runoff processes, hydraulic routing, floodplain inundation, and flood-defence failure mechanisms
• Calibrate and validate model components with a focus on GIS, using observed data, published research, and benchmark datasets, including comparisons between simulated and observed flood extents and depths
• Evaluate model outputs such as flood footprints, hazard metrics, and loss patterns using statistical methods, visualisation, and GIS tools
• Advance flood model input layer preparation, including elevation models, land use data, and remotely sensed data, through research, evaluation, and implementation of novel scientific and computational techniques
• Explore, prototype, and evaluate AI and ML methods for model-development challenges including surrogate modelling, spatial downscaling, feature extraction, anomaly detection, data processing, emulator development, and workflow automation
• Contribute to the development of reproducible scientific workflows, code repositories, and data-processing pipelines using modern software development practices
• Communicate research findings and modelling insights to internal stakeholders, clients, and the broader scientific community
• Contribute to an inclusive, collaborative culture through knowledge sharing, constructive review, and continuous learning

About the Team

Our Model Development team brings together scientists, engineers, and data specialists to build natural catastrophe models for hazards including tropical and extra-tropical storms, winter storms and convective storms, coastal and inland flooding, and tsunamis. The Inland Flood team develops high-resolution, large-scale hydrologic and hydraulic models spanning hazard through loss, combining observations, reanalysis and climate data, numerical and statistical modelling, engineering methods, and data assimilation. As AI and machine learning evolve, we integrate them selectively alongside established physical and statistical approaches to improve model development, data processing, quality assurance, and scientific insight. By joining the team, you will contribute to meaningful scientific work, grow as a GIS specialist or flood modeller, and help advance Moodys position as a global leader in catastrophe risk assessment.

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