Intern – Yield Prediction

DNEXT is a market data intelligence platform for the commodity players. Our software aggregates data from hundreds of sources including imports, exports, weather, crop production, prices, etc. By connecting the dots across fragmented information landscapes, we bring our clients – mostly trading houses, hedge funds, food producers – unique, real-time understanding of supply and demand in the commodity markets.

We are looking for an intern to use weather and remote sensing data to build machine learning models to predict crop yields at different stages during the growing season and


You will analyse weather and satellite data as well as historical yield data for several different crops and use this data to produce a set of features to be train different machine learning models and compare their efficiency at predicting end-of –season crop yields. The resulting machine learning model will provide in-season predictions and analysis of the feature importance of different inputs to the yield prediction model.

The work will be done in collaboration with crop analysts and data scientists who will provide help accessing and analysing data as well as guidance regarding the analysis of crop production data.


  • Advanced degree, or working towards degree in STEM subject
  • Proficient in Python, familiar with Python tools and data formats used to analyse data and build machine learning models (pandas, scikitlearn, pytorch, Stan, etc.)
  • Knowledge of applied statistics and basic experience analysing weather and environmental data, experience analysing time series data
  • Bonus: basic knowledge of agriculture, meteorology, remote sensing
  • Ability to teamwork, genuine interest in learning more about agriculture and commodity markets

To apply for this job email your details to