
Senior Machine Learning Scientist
- Enkhuizen, Noord-Holland
- Vast
- Voltijds
- Designing and implementing innovative machine learning solutions to solve complex agricultural and environmental challenges that often require novel approaches.
- Developing and optimizing predictive models using structured historical cross-functional data from field trials, laboratory experiments, and environmental measurements.
- Translating agricultural/environmental problems into data science solutions in collaboration with domain experts.
- Supporting experimental design to ensure data suitability for modeling purposes.
- Working with IT teams on end-to-end ML projects from data preparation to model deployment and monitoring.
- Presenting insights and recommendations to stakeholders across different functions.
- Engaging with high-priority digital transformation projects to understand opportunities to accelerate the impact of data science for predictive field trialing.
- PhD in Computer Science, Data Science, Machine Learning, or related field (or M.Sc. with extensive hands-on ML experience).
- Proven experience in developing machine learning models, particularly:
- Supervised learning (Random Forests, XGBoost, SVM etc.).
- Clustering and dimensionality reduction.
- Time series analysis.
- Handling hierarchical/nested data structures.
- Strong programming skills in Python and experience with ML frameworks (scikit-learn, pandas, etc.).
- Demonstrated ability to develop novel solutions beyond applying existing frameworks.
- Creative problem-solving mindset and ability to think outside standard approaches.
- Experience in adapting and modifying algorithms to suit specific problem requirements.
- Passion for emerging technologies, including Generative AI and other innovative approaches.
- Strong communication skills to explain technical concepts to non-technical stakeholders.
- Ability to visualize and story-tell with data to communicate results to parties with varying levels of technical proficiency.
- Interest in agriculture, environmental science, or related fields.
- Experience with version control (Git) and ML experiment tracking.
- Experience working in cross-functional teams.
- Familiarity with experimental design or statistical analysis.
- Experience with cloud computing platforms (AWS, Azure, etc.) and querying (e.g. SQL).