AccuWeather is seeking to enhance its life-saving weather intelligence systems, particularly the AccuWeather Lightning Network™, by applying data science, AI, and meteorological expertise to improve detection accuracy, reliability, and decision-making tools used by millions.
Requirements
- 2–5 years of experience applying meteorological algorithms, models, or similar technologies (or equivalent academic/research experience).
- Familiarity with meteorological datasets, data transformation, and quality control methods.
- Exposure to remote sensing data (satellite, radar) and interest in expanding lightning detection expertise.
- Proficiency in programming languages such as Python, SQL, or C++ for analysis, modeling, and automation.
- Familiarity with big data technologies (Spark, Hadoop) to process large datasets.
- Strong understanding of data science fundamentals: statistical analysis, ML concepts, geospatial analytics.
- Ability to work collaboratively in multidisciplinary teams and contribute scientific/technical insights.
Responsibilities
- Enhance the AccuWeather Lightning Network™ by developing workflows that improve detection rates, location accuracy, and false alarm reduction.
- Integrate meteorological expertise into modeling frameworks, data pipelines, and scalable operational weather systems.
- Design and implement verification methods to assess performance of lightning detection systems using observational, satellite, and reanalysis datasets.
- Collaborate with Data Scientists, ML Engineers, and Applied Meteorologists to improve predictive models and decision-support tools.
- Analyze and validate lightning and weather-related datasets, identifying areas for improvement and model enhancements.
- Support ETL processes with data engineering teams, ensuring data quality, meteorological consistency, and standardization.
- Explore new AI/ML and geospatial approaches through research and prototyping to improve detection and forecasting.
Other
- Strong communication skills for presenting findings to diverse audiences.
- Demonstrated curiosity and initiative in exploring new scientific methods, technologies, or datasets.
- Ability to work collaboratively in multidisciplinary teams.
- Maintain clear documentation, reproducible workflows, and contribute to team research reviews.
- Communicate findings to both technical and non-technical stakeholders, translating complex science into actionable insights.
