Key Responsibilities and Required Skills for Weather Trainer

🎯 Role Definition

The Weather Trainer is a hybrid technical and domain expert responsible for developing, training, validating, and operationalizing machine‑learning and numerical weather prediction (NWP) models while also designing and delivering training programs for meteorologists, forecasters, and operations staff. This role combines meteorological expertise (satellite/radar interpretation, synoptic analysis, thermodynamics) with practical ML and MLOps skills (data ingestion, model training, hyperparameter tuning, evaluation, CI/CD and deployment) to improve forecast accuracy, nowcasting, and decision support systems. The ideal Weather Trainer drives end‑to‑end model development, ensures model reliability in production environments, provides interpretability and verification metrics, and equips internal teams and external partners with the knowledge and tools to use models effectively.

📈 Career Progression

Typical Career Path

Entry Point From:

Meteorologist / Operational Forecaster moving into applied ML and model training.
Data Scientist or Machine Learning Engineer with experience in time series and geospatial data.
Atmospheric Scientist or Research Scientist focused on numerical modelling and remote sensing.

Advancement To:

Senior Weather Trainer / Lead Modeler
Forecasting & Modeling Manager
Head of Applied Meteorology or Director of Weather AI
Principal Scientist — Atmospheric Modeling and AI

Lateral Moves:

Machine Learning Engineer (specializing in environmental data)
MLOps Engineer for climate and weather applications
Product Manager for weather intelligence products

Core Responsibilities

Primary Functions

Own the end‑to‑end process for training weather forecasting models, including data collection and curation of multi‑source meteorological datasets (radar, satellite, reanalysis, sounding, surface observations), feature engineering for spatio‑temporal inputs, and creation of labeled training sets for supervised learning.
Design and implement robust model training pipelines for nowcasting, short‑range and medium‑range forecasts using deep learning (CNNs, RNNs, Transformer architectures) and classical machine learning (gradient boosting, random forests) with focus on precipitation, wind, temperature, and severe weather detection.
Perform data assimilation and pre‑processing tasks required for NWP and hybrid ML‑NWP systems, including bias correction, temporal/spatial resampling, geospatial reprojections, quality control, and missing value imputation to ensure model input integrity.
Lead hyperparameter tuning, cross‑validation, and model selection experiments using reproducible workflows and experiment tracking tools (e.g., MLflow, Weights & Biases), and translate experimental results into production ready model artifacts.
Develop and maintain model evaluation and verification frameworks tailored to meteorological needs (e.g., RMSE, MAE, CRPS, Brier Score, ROC, POD, FAR, equitable threat score), including the creation of automated verification reports and dashboards for stakeholders.
Implement ensemble modeling strategies, probabilistic forecasts, and post‑processing/calibration techniques (quantile regression, Bayesian model averaging, EMOS) to provide calibrated uncertainty estimates for decision support.
Containerize training and inference workflows (Docker) and integrate with orchestration systems (Kubernetes, Airflow) to enable scalable training on cloud or HPC environments and ensure reliable model retraining schedules.
Design and run operational retraining and model update pipelines, including scheduled re‑training, drift detection, and rollback procedures, to keep deployed models current with seasonal and climate shifts.
Integrate remote sensing and radar reflectivity products into training pipelines using domain‑specific preprocessing (e.g., attenuation correction, clutter removal, rain‑rate conversion) to improve convective and precipitation nowcasts.
Implement techniques for spatio‑temporal modeling (convLSTM, 3D convolutions, graph neural networks) and multi‑modal fusion to capture the complex dynamics of atmospheric processes across scales.
Work closely with forecasters, product managers, and end users to translate operational requirements into model objectives, acceptance criteria, and user‑facing performance metrics.
Create technical documentation, model cards, and reproducible notebooks that explain model architecture choices, training datasets, limitations, and recommended operational use cases to both technical and non‑technical stakeholders.
Lead cross‑functional training sessions, workshops, and hands‑on bootcamps to upskill meteorologists and operations staff in model interpretation, verification metrics, and best practices for leveraging ML outputs in forecast decision making.
Conduct ablation studies and feature importance analyses (SHAP, LIME, saliency maps) to improve model interpretability and to surface potential biases or failure modes relevant to safety‑critical weather operations.
Implement quality assurance and validation steps for model inputs and outputs, including automated anomaly detection for incoming observational streams and sanity checks for forecast consistency across lead times.
Collaborate with software engineers and MLOps teams to build repeatable CI/CD pipelines for model testing, validation, and deployment, ensuring traceability of model versions, data snapshots, and hyperparameters.
Manage data labeling projects for supervised learning, including designing annotation schemas, recruiting/leading labelers or using active learning strategies, and validating label quality for events such as convective initiation, icing, or visibility reductions.
Evaluate and benchmark third‑party models, pre-trained weights, and NWP outputs against in‑house models and operational baselines to inform model selection and procurement decisions.
Participate in research and applied development to experiment with emerging architectures, domain adaptation, transfer learning, and semi‑supervised techniques to combat data scarcity for rare weather events.
Support operational incident response and root cause analysis when model performance degrades in production, coordinating rollbacks, hotfixes, and communication to impacted stakeholders.
Ensure compliance with data governance, privacy, and licensing requirements when sourcing or sharing proprietary observational datasets, and maintain metadata and provenance for reproducibility.
Mentor junior modelers and scientists, provide code reviews, and establish best practices for reproducible research and production‑grade model development.
Develop and present periodic performance summaries and forecasts of model skill improvements to executive stakeholders, highlighting ROI, risk reduction, and product impact.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Assist customer success and sales teams in technical demos, PoCs, and pilot deployments of forecasting solutions.
Maintain user guides, FAQ, and quick reference materials for forecast model consumers and partner organizations.

Required Skills & Competencies

Hard Skills (Technical)

Advanced proficiency in Python and scientific computing libraries (NumPy, SciPy, pandas, xarray) for spatio‑temporal data handling.
Experience with deep learning frameworks (TensorFlow, Keras, PyTorch) and model training best practices for computer vision and time‑series forecasting.
Strong knowledge of numerical weather prediction (NWP) concepts, data assimilation, and operational model outputs (ECMWF, GFS, NAM) and how to combine them with ML systems.
Practical experience processing and using remote sensing and radar datasets (GOES, Himawari, Sentinel, NEXRAD) including ingestion, preprocessing, and feature extraction.
Familiarity with ensemble forecasting, probabilistic methods, calibration techniques, and verification metrics specific to meteorology (CRPS, Brier Score, ETS).
MLOps and production deployment skills: Docker, Kubernetes, CI/CD, model serving frameworks (TensorFlow Serving, TorchServe), and experiment tracking tools.
Experience with time series modeling, sequence architectures (LSTM, GRU, convLSTM), and spatio‑temporal neural networks.
Proficiency with Linux environments, batch schedulers / HPC systems, cloud platforms (AWS, GCP, Azure) and related services for scalable model training and data storage.
SQL and experience with big data tools (Spark, Dask) for querying and processing large observational archives.
Familiarity with GIS tools and geospatial libraries (GDAL, rasterio, geopandas) and projection/coordinate transformations.
Experience with model interpretability and explainability tools (SHAP, LIME, saliency maps) and building model cards for governance.
Version control (Git), code quality practices, unit testing for reproducible pipelines, and documentation standards.
Optional/Beneficial: knowledge of Fortran/C++ for interfacing with legacy NWP codebases, experience with post‑processing tools (MetPy), and understanding of sensor error characteristics.

Soft Skills

Strong meteorological intuition and the ability to translate domain knowledge into model features and evaluation criteria.
Excellent verbal and written communication skills to explain complex model behavior to forecasters, stakeholders, and non‑technical audiences.
Collaborative mindset, able to work cross‑functionally with data engineers, product managers, and operational teams.
Problem‑solving orientation with attention to detail in validation, verification, and quality assurance processes.
Project management and prioritization skills; able to balance model development, retraining cadence, and operational commitments.
Teaching and mentoring ability to design effective workshops, training materials, and on‑the‑job learning experiences.
Adaptability and resilience in fast‑paced operational settings where model performance directly impacts decision making.
Ethical awareness and capacity to evaluate model biases and fairness concerns in weather impact forecasting.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Atmospheric Science, Meteorology, Computer Science, Data Science, Applied Mathematics, or a related quantitative field.

Preferred Education:

Master’s or PhD in Atmospheric Science, Meteorology, Machine Learning, Artificial Intelligence, or a closely related discipline with applied experience in environmental forecasting.

Relevant Fields of Study:

Meteorology / Atmospheric Sciences
Computer Science / Machine Learning
Data Science / Statistics
Physics / Applied Mathematics
Geoscience / Remote Sensing

Experience Requirements

Typical Experience Range: 3–8 years of combined experience in meteorology, model development, or applied machine learning with at least 2 years focused on weather or environmental datasets.

Preferred:

5+ years working on operational forecasting systems, NWP, or applied ML for geoscience applications.
Demonstrated track record of shipping models to production, automating retraining, and supporting operational use during high‑impact weather events.
Prior experience training or mentoring forecasters or end users on model outputs and interpretation.