UrtheCast

Data Scientist

• Perform complex statistical and comparative analysis on large-scale weather datasets including BRIB2, NetCDF, and NEMSIO formats from multiple data sources
• Develop comprehensive validation frameworks to assess the accuracy and skill of weather forecasting models against publicly available operational models and observational data
• Conduct forecast verification studies using standard meteorological metrics (RMSE, ACC, bias, skill scores) across multiple atmospheric variables and forecast lead times
• Analyze and document the strengths, limitations, and performance characteristics of major operational weather models (GFS, GEFS, CFS, ECMWF IFS, ERA5)
• Identify forecast biases, systematic errors, and areas for improvement in weather prediction systems
• Evaluate the impact of different initialization times, resolutions, and parameterizations on forecast quality
• Write robust, production-quality Python code following software engineering best practices for weather data processing, analysis, and model evaluation
• Develop and maintain scalable data pipelines to ingest, process, and analyze meteorological data from multiple sources in various formats (GRIB2, NetCDF, NEMSIO)
• Integrate analysis scripts and machine learning models into existing production codebase using modern development workflows
• Deploy cloud-based solutions to AWS using AWS CDK (Cloud Development Kit) and infrastructure-as-code principles
• Implement MLOps best practices including model versioning, experiment tracking, monitoring, and automated retraining pipelines
• Build CI/CD pipelines for continuous integration and deployment of forecasting models and data processing workflows
• Optimize code performance for handling large-scale meteorological datasets efficiently
• Design, develop, and deploy AI-based weather forecasting models using machine learning and deep learning techniques
• Research and implement state-of-the-art approaches in AI weather prediction including neural networks, graph neural networks, transformers, and generative models
• Evaluate emerging AI weather models (e.g., ECMWF AIFS) and assess their applicability to business use cases
• Develop hybrid forecasting approaches that combine physics-based numerical weather prediction with data-driven machine learning methods
• Train models on large historical weather datasets (ERA5, HRRR, GFS archives) using distributed computing resources
• Implement probabilistic and ensemble forecasting techniques using machine learning to quantify forecast uncertainty
• Optimize model architectures for computational efficiency and forecast skill
• Develop derived meteorological features and indices from raw weather data that provide value for industry-specific applications

Senior Data Scientist – Numerical Weather Prediction

Cloud Engineer

• Architect cloud-native solutions bridging traditional HPC paradigms with modern DevOps practices.
• Design, architect, and deploy scalable high-performance computing (HPC) solutions on AWS.
• Configure and optimize HPC job schedulers for resource allocation and workload management.
• Troubleshoot and resolve complex issues with AWS HPC clusters.
• Build and maintain cloud-native applications using AWS PaaS services.
• Create and manage containerized applications using Docker for deployment across environments.
• Design and execute machine image replication and migration strategies from AWS to provider-agnostic HPC cloud platforms.
• Architect database schemas for optimal performance, scalability, and data integrity.
• Implement comprehensive monitoring, logging, and alerting solutions using AWS tools.
• Build and maintain CI/CD pipelines for infrastructure and application deployments.

Senior Data Scientist – Numerical Weather Prediction

Senior HPC Cloud Engineer