Requirements at a glance

5+ years Data Science (Required)
Advanced Degree Engineering or Science (Required)
Machine Learning (Required)
Python (Required)
Scikit and Pytorch (Required)
MLflow and DVC (Required)
Sensor data processing (Required)
English (Required)
Docker, CI/CD, AWS or GCP (Preferred)

RequiredPreferred

Today

Senior/Principal Data Scientist (m/f/d)

Potsdam

Full-time

Permanent employee

Who are we?

At Stenon we are on a mission to democratize soil data. We have become the global market leader in real-time, digital soil data. This provides farmers with essential insights to make optimal and sustainable cultivation decisions, boosting yield, crop quality, and soil health while saving money on inputs. Stenon has closed an $18 million Series B financing round to scale real-time nitrogen and soil data management and is expanding internationally.

Position

We seek a versatile and experienced Senior Data Scientist, that drives and expands our existing soil nutrient machine learning as well as new data-driven features that support our customers in making better agronomic decisions for their fields using real-time soil data.

You will

Be responsible for a core pillar of Stenon's products
Scale our soil nutrient modelling to support more and more regions across the world
Oversee planning, execution and delivery of Data Science projects.
Contribute to the Data Science strategy, vision and roadmap, enhancing Stenon's agronomy intelligence layer to support additional features and enable new data sources
Work with a variety of data from spectral sensor data via chemical lab analysis results to spatial fertilization maps.

You have

At least 5+ years of hands-on experience in Data Science products
Advanced Degree in Engineering or Science
Exceptional understanding of statistical and fundamental machine learning concepts
Experience writing and maintaining python code, incl. common libraries like scikit, pytorch as well as ML-Ops infrastructure like MLflow, DVC
High degree of autonomy
Ability to translate complex statistical or machine learning concepts for non-technical audiences