OUR VISION
At EarthDaily 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 EarthDaily 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 EarthPipeline 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 TEAM
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!

READY TO LAUNCH?
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.

RESPONSIBILITIES

Weather Model Analysis & Validation

• 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

Production-Grade Development & MLOps

• 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

AI Weather Forecasting & Machine Learning

• 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

Feature Engineering & Domain Expertise

• Develop derived meteorological features and indices from raw weather data that provide value for industry-specific applications
• Create domain-specific weather variables and aggregations tailored to energy markets, agriculture, insurance, logistics, and other weather-sensitive industries
• Transform complex atmospheric data into actionable insights and decision-support products for commercial applications
• Design weather indices and composite variables that correlate with business outcomes and market dynamics
• Engineer features for machine learning models that capture relevant meteorological patterns and relationships

Subject Matter Expertise & Communication

• Convey deep technical knowledge about publicly available weather models, reanalysis datasets, and forecasting systems to both technical and non-technical audiences
• Articulate how different weather models are used across various industries for market intelligence, risk management, and operational decision-making
• Provide expert guidance on the capabilities, limitations, and appropriate use cases for different weather data products and forecasting systems
• Answer specific and challenging technical questions posed by clients and prospects during sales presentations and discovery calls
• Create technical documentation, presentations, and visualizations that communicate complex meteorological concepts clearly
• Collaborate with sales and product teams to translate customer weather data needs into technical solutions

Data Processing & Analysis

• Programmatically manipulate and analyze meteorological data formats using specialized libraries (xarray, cfgrib, pygrib, wgrib2)
• Process multi-dimensional weather datasets with temporal and spatial components efficiently at scale
• Perform exploratory data analysis (EDA) on large weather archives to identify patterns, trends, and anomalies
• Conduct spatial and temporal aggregations, interpolations, and regridding operations on gridded weather data
• Quality-control and validate meteorological datasets to ensure data integrity and accuracy
• Develop automated data processing workflows for routine analysis and monitoring tasks

Research & Innovation

• Stay current with the latest developments in numerical weather prediction, AI weather forecasting, and atmospheric science research
• Evaluate new data sources, weather models, and forecasting techniques for potential integration into products and services
• Conduct applied research to advance weather forecasting capabilities and develop proprietary methodologies
• Contribute to technical publications, white papers, and thought leadership content in weather and climate science

Other Duties As Assigned

YOUR PAST MISSIONS

• Master's degree or Ph.D. in Atmospheric Science, Meteorology, Climate Science, Computational Science, Data Science, Physics, or closely related quantitative field with focus on weather/climate applications
• 5-8 years of professional experience in atmospheric science, weather forecasting, climate modeling, or closely related fields
• 3+ years of hands-on experience working with operational numerical weather prediction models (GFS, GEFS, CFS, ECMWF IFS, ERA5, or similar)
• 3+ years of production-level Python development for scientific computing, data analysis, and machine learning applications
• Demonstrated experience processing and analyzing large-scale meteorological datasets in GRIB2, NetCDF, or NEMSIO formats
• Proven track record of developing and deploying machine learning models or data science solutions in production environments
• Experience with forecast verification, model evaluation, and statistical analysis of weather prediction systems
• Strong portfolio demonstrating weather data analysis, visualization, and modeling projects

Nice To Haves:

• Ph.D. in Atmospheric Science, Meteorology, or related field with research focus on numerical weather prediction, data assimilation, or weather forecasting
• Advanced coursework or specialization in machine learning, statistical modeling, or computational methods applied to atmospheric science
• Experience developing or working with AI-based weather forecasting models
• Background in weather model development, data assimilation, or numerical methods for atmospheric modeling
• Experience in energy commodities markets (natural gas, power, heating oil) or agricultural commodities
• Familiarity with weather derivatives, weather risk management, or climate risk analytics
• Experience presenting technical weather information to non-technical business audiences

YOUR TOOLKIT

Core Technical Skills (Required):

• Programming & Development: Python (expert level), NumPy, Pandas, xarray, Dask, SciPy, scikit-learn
• Weather Data Processing: GRIB2, NetCDF, NEMSIO formats; libraries including cfgrib, pygrib, wgrib2, netCDF4, xarray
• Machine Learning: PyTorch or TensorFlow, deep learning architectures, model training and evaluation, MLOps practices
• Statistical Analysis: Forecast verification methods, skill scores (RMSE, MAE, ACC, bias), hypothesis testing, time series analysis
• Atmospheric Science: Deep understanding of atmospheric dynamics, weather forecasting principles, model physics and parameterizations
• Cloud Computing: AWS services (S3, EC2, Lambda, SageMaker, ECS), cloud-based data processing and model deployment
• Version Control & Collaboration: Git, GitHub/GitLab, code review practices, collaborative development workflows
• Data Visualization: Matplotlib, Cartopy, Plotly, creating publication-quality figures and maps

Weather Model Expertise (Required):

• NOAA Models: GFS (Global Forecast System), GEFS (Global Ensemble Forecast System), CFS (Climate Forecast System)
• ECMWF Systems: ERA5 reanalysis, IFS (Integrated Forecasting System), AIFS (Artificial Intelligence Forecasting System)
• Model Architecture: Understanding of model initialization, data assimilation, physics parameterizations, and ensemble methods
• Weather Variables: Comprehensive knowledge of atmospheric variables (temperature, pressure, wind, humidity, precipitation, radiation)
• Forecast Products: Familiarity with surface fields, pressure levels, derived products, and specialized outputs

Additional Technical Skills (Highly Valued):

• AI Weather Models: Hands-on experience with GraphCast, ECMWF AIFS, Pangu-Weather, FourCastNet, or similar neural network forecasting systems
• Infrastructure as Code: AWS CDK, Terraform, CloudFormation for reproducible deployments
• Containerization: Docker, container orchestration for model deployment
• Big Data Technologies: Apache Spark, distributed computing frameworks for large-scale data processing
• Advanced ML: Graph neural networks, transformers, generative models, diffusion models
• HPC Experience: Working with high-performance computing systems, job schedulers (Slurm), parallel computing
• Numerical Methods: Experience with numerical modeling, finite difference methods, or atmospheric model development
• Additional Languages: R for statistical analysis, Julia for scientific computing, or Fortran for legacy code integration

Domain Knowledge & Business Acumen:

• Understanding of how weather impacts energy markets, agriculture, insurance, logistics, and other weather-sensitive industries
• Knowledge of weather derivatives, degree days (heating/cooling), and weather-based indices
• Ability to translate meteorological insights into business value and commercial applications

Problem-Solving & Analysis:

• Advanced analytical thinking and troubleshooting complex technical problems
• Critical evaluation of model performance and identification of improvement opportunities
• Ability to formulate research questions and design experiments to test hypotheses
• Creative approaches to feature engineering and deriving value from weather data

Communication & Collaboration:

• Exceptional ability to explain complex meteorological and technical concepts to diverse audiences
• Strong technical writing skills for documentation, reports, and presentations
• Comfortable presenting to clients and technical peers
• Collaborative mindset for working with cross-functional teams including engineering, product, and sales
• Active listening skills to understand client needs and translate them into technical requirements

Soft Skills:

• Self-motivated and able to work independently with minimal supervision in a remote environment
• Strong organizational and time management skills to handle multiple projects simultaneously
• Intellectual curiosity and passion for continuous learning in weather science and machine learning
• Adaptability to rapidly changing technologies, business priorities, and client requirements
• Customer-focused approach with commitment to delivering high-quality solutions

OUR SPACE (including travel)
We’d love to welcome you to the EarthInsights team for this fully-remote opportunity open to individuals in and working from the US and Canada. Agile software development with daily standups and weekly Scrum cadence in a fast-paced environment with the need to adapt quickly to time-sensitive deliveries.
Ours is a fun, fast-paced and exciting work environment where we hold earth-smart (living sustainably), creativity and innovation, proactive communication, diversity and accountability as core values. And just like space exploration - we’re constantly evolving and pushing new boundaries.
Hours of work typically fall between 9:00am and 5:00pm Central Time Monday to Friday with periodic cross-over work required with other team members across a few times zones in addition to occasional evening and weekend work. Team members need be available for a minimum of six (6) hours daily during this period to facilitate collaboration.

YOUR COMPENSATION
Base Salary Range: $145 - 180K USD annually

The range above depends on job-related skills, experience, training, education, location and business needs. The range is based on Minneapolis-derived compensation for this role. Only when a candidate has the demonstrated experience, skills, and expertise to advance in the range for this position, would we consider paying at the top end of the range for this role.

WHY EARTHDAILY ANALYTICS?

• Competitive compensation and flexible time off
• Be part of a meaningful mission in one of North America's most innovative space companies developing sustainable solutions for our planet
• Great work environment and team with head office locations in Vancouver, Canada and Minneapolis, MN