Company Description
Syngenta is a Global Agribusiness Company with its headquarters in Basel, Switzerland. We are a market leader in the breeding and production of Seeds and Cuttings for high-quality pot and bedding plants. In Kenya, we have two production Sites; Kenya Cuttings Limited based in Thika, and Pollen Limited based in Ruiru.
Job Description
- Ensure effective and efficient data collection, analysis, and interpretation of large datasets to help drive strategic decision-making across the organisation
- Develop and maintain dashboards, reports, and other visualisations from complex data models
- Perform statistical analysis and create models to support business decisions
- Collaborate with other teams to integrate data from different sources
- Support with improvement and modelling of the Current Power BI reports
- Build and utilise advanced data models to forecast future production situations; Identify, analyse, and interpret trends or patterns in complex production data sets
- Support bottom-up Production material planning and forecasting processes in liaison with other departments
- Assist in the development of production material consumption, planning and forecasts, ensuring accuracy and consistency
- Identify and implement process improvements
- Streamline Data, KPI and Production material planning processes and workflows using technology and best practices
- Stay up to date with industry trends and best practices
- Continuously learn and apply new tools and techniques to enhance data2infor capabilities
Qualifications
Critical knowledge:
- Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Analysis or related field
- Highly skilled/advanced in Microsoft Office (especially Excel and PowerPoint), Power BI, SQL or Dax language, SAP and SAC
Critical experience:
- 3+ years hands-on advanced analytics/visualisation development experience in Power BI (preferred), Qlik or other reporting tools
- 2+ years of experience with Python, SQL, or other transformation tools for data analysis, preparation, and modelling, including experience with large and unstructured data sets
- Experience of working and interacting across multiple locations and cultures