Vitus Commodities actively trades electricity and natural gas contracts in global markets. We are seeking a Meteorological Data Scientist / Quantitative Analyst to join our computational meteorology team to support energy trading operations.
Job Description
As a Meteorological Data Scientist / Quantitative Analyst, you will develop and maintain robust analytical models, transforming large-scale meteorological data into actionable insights for renewable energy forecasting and market strategy. This is a collaborative, fast-paced environment at the intersection of data science, meteorology, and energy markets.
Key Responsibilities
* Develop and maintain statistical and machine learning models for energy forecasting and market analysis
* Build and optimize ETL pipelines for meteorological data ingestion, feature engineering, and storage
* Transform large and diverse datasets into actionable business insights
* Monitor, evaluate, and improve model performance and data reliability
* Design and write highly efficient, optimized code with a focus on minimizing latency and maximizing computational performance
* Contribute to the automation and reliability of our data and modeling infrastructure
* Collaborate effectively within a technical team and support innovation in analytics and modeling
Qualifications
* Experience with end-to-end data pipelines: data ingestion, preprocessing, feature engineering, modeling, and outputting to storage (e.g., Parquet, databases).
* Demonstrated skill in developing and maintaining statistical or machine learning models (forecasting, regression, classification).
* Strong data wrangling and automation abilities
* Proven experience designing efficient, high-performance code for low-latency applications
* Effective communication skills and a collaborative, solution-oriented mindset
* Strong proficiency in R (tidyverse, data.table, modeling packages), and experience with meteorological data formats (GRIB, NetCDF) is highly preferred
* Experience with Python (pandas, xarray, pygrib, cfgrib) is advantageous
* Background in meteorology, climate science, or spatial data analysis is a plus
* Familiarity with energy market or trading data is a plus