Senior Data Scientist - Numerical Weather Prediction

Overview

We are currently accepting applications for this open role to build a qualified pipeline. Recruitment for this position is anticipated to start in early 2026 (Q1). Thank-you for your patience.

At Earth Daily Analytics  (EDA), we strive to build a more sustainable planet by creating innovative solutions that combine satellite imagery of the Earth, modern software engineering, machine learning, and cloud computing to solve the toughest challenges in agriculture, energy and mining, insurance and risk mitigation, wildfire and forest intelligence, carbon-capture verification and more.

EDA’s signature Earth Observation mission, the Earth Daily Constellation (EDC), is currently under construction. The EDC will be the most powerful global change detection and change monitoring system ever developed, capable of generating unprecedented predictive analytics and insights. It will combine with the Earth Pipeline data processing system to provide unprecedented, scientific-grade data of the world every day, positioning EDA to meet the growing needs of diverse industries.

Our global, enterprise-wide team represents a variety of business lines and is made up of business development, sales, marketing and support professionals, data scientists, software engineers, project managers and finance, HR, and IT professionals. Our Earth Insights team is nimble and collaborative, and in preparation for launching a frontier and disruptive product in EDC, we are currently looking for an experienced Senior Data Scientist - Numerical Weather Prediction to join our crew, serving as a technical expert and thought leader in weather and climate modeling, bridging the gap between cutting‑edge atmospheric science and commercial applications!

Do you want to work for one of the most exciting space companies at the forefront of global change detection/change monitoring, and the intersection of atmospheric science, data science, and machine learning? The ideal candidate will have deep expertise in operational weather models, enabling them to evaluate forecast accuracy, develop sophisticated validation frameworks, and extract actionable insights from massive meteorological datasets.

This role demands a deep technical understanding and astute business acumen, allowing you to both build production‑grad AI forecasting models and communicate complex meteorological concepts to clients, assisting in the sales process.

• Perform complex statistical and comparative analysis on large‑scale weather datasets including GRIB2, 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…