AccuWeather

Data Scientist II

AccuWeather

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Job Level

JuniorMid-Level

Tech Stack

ETLHadoopPythonRemote SensingRokuSparkSQL

About the role

  • 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.
  • Communicate findings to both technical and non-technical stakeholders, translating complex science into actionable insights.
  • 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.
  • Participate in cross-functional projects applying lightning and weather insights to business use cases.
  • Explore new AI/ML and geospatial approaches through research and prototyping to improve detection and forecasting.
  • Maintain clear documentation, reproducible workflows, and contribute to team research reviews.

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.
  • Strong communication skills for presenting findings to diverse audiences.
  • Demonstrated curiosity and initiative in exploring new scientific methods, technologies, or datasets