Data Scientist

Key Responsibilities:

• Develop and implement machine learning algorithms, statistical models and analytical scripts to address business challenges.

• Perform data collection, transformation, integration and processing from multiple data sources.

• Conduct statistical analysis, experiment design, A/B testing, cross-validation and model performance evaluation.

• Analyse large datasets to identify trends, patterns, anomalies and business insights.

• Develop interactive dashboards, reports and data visualisations using Power BI, R and Python.

• Work with SQL databases and other data storage technologies to extract, process and analyse data.

• Access and integrate external data sources through APIs.

• Implement data validation processes, quality checks and automated data monitoring.

• Design and support data acceptance testing and user acceptance testing (UAT) for data-related IT solutions.

• Ensure compliance with data governance principles and emerging EU AI Act requirements.

• Monitor machine learning models for performance, bias and data drift, maintaining appropriate documentation.

• Prepare analytical reports, technical documentation, templates and user guidance materials.

• Collaborate with IT teams, data providers, business stakeholders and international project teams.

• Translate complex analytical findings into clear business insights and actionable recommendations.

Required Technical Skills:

• Strong expertise in Data Science, Data Analytics and Statistical Modelling.

• Hands-on experience with Python, R and SQL.

• Practical experience with Power BI, Excel, SQL Developer and RStudio.

• Experience in data visualisation, trend analysis and anomaly detection.

• Knowledge of database technologies such as SQL, NoSQL, MongoDB and Hadoop.

• Understanding of scalable data storage solutions, including Data Lakes, Lakehouses and distributed data stores.

• Experience with API-based data access and integration.

• Knowledge of machine learning algorithms, predictive analytics and advanced analytics techniques.

• Understanding of AI governance, model explainability, fairness and accountability.

• Experience with data quality management, validation and acceptance testing.

Essential Experience & Qualifications:

• University degree at EQF Level 7 (Master's degree) with at least 5 years of IT experience; OR EQF Level 6 (Bachelor's degree) with at least 7 years of IT experience.

• Minimum 7 years of experience in Data Science or Data Analytics.

• Minimum 1 year of experience with data management and visualisation tools in the agriculture domain.

• At least 5 years of experience in data analysis or data science.

• Minimum 2 years of experience working with Python, R, SQL, Power BI and Excel.

• At least 2 years of experience in API-based data access.

• English proficiency at B2 level or above.