Berry Yield Forecaster - EU Data Scientist (Agronomy)

Bitwise Agronomy; Arbo; Presencial; EUR 55.000 - 75.000; Jornada completa

Descripción de la vacante

Bitwise Agronomy in Spain seeks a data scientist focused on agronomy to translate complex crop data into actionable yield forecasts. You will profile datasets, fix quality issues, and build reproducible models that others can run later, while communicating results to non-technical stakeholders.

You will lead discussions with customers, support forecast refinements, and contribute to team growth in a fast-paced environment across time zones.

Formación

• Bachelor’s or Master’s degree in Agronomy, Agricultural Science, Data Science, Statistics, or a related field.
• Hands-on experience in commercial berry production.
• High proficiency in Google Sheets and Excel; Python experience.
• Experience using Python and AI tools for data analysis and model development.
• Experience in crop yield forecasting is highly desired.
• Fluency in English is essential.

Responsabilidades

• Translate complex data sets into accurate yield predictions.
• Profile datasets, identify quality problems, and fix or flag them loudly.
• Make the work reproducible with reusable queries and scripts.
• Communicate results as a one-pager, chart, or concise explanation for non-technical stakeholders.
• Lead customer discussions to refine forecasts based on inputs.

Conocimientos

Fluent English

Excel/Google Sheets

Python

AI tools

Data analysis

Customer communication

Educación

Bachelor’s or Master’s in Agronomy, Agricultural Science, Data Science or Statistics

Herramientas

Claude

Copilot

ChatGPT

Power Query

Descripción del empleo

Bitwise Agronomy in Spain seeks a data scientist focused on agronomy to translate complex crop data into actionable yield forecasts. You will profile datasets, fix quality issues, and build reproducible models that others can run later, while communicating results to non-technical stakeholders.

You will lead discussions with customers, support forecast refinements, and contribute to team growth in a fast-paced environment across time zones.