Role purpose

• The Production Research Data Analyst role is responsible for supporting the success of delivering the pipeline and data insights. This role will support activities related to data governance, analysis and reporting to internal and external stakeholders across Africa. Furthermore, responsibilities include adherence to Bayer safety, quality, and operational procedures.

Key responsibilities and tasks

• Building and validating dashboards using preferred visualization software.
• Compiling documentation of analytical processes and results, adhering to agreed documentation standards.
• Supporting process data mining from IT systems and databases.
• Extract actionable insights from raw data.
• Performing quality control on collected Production Research data.
• Performing statistical data analyses to answer specific research questions.
• Providing analytics support to various projects locally.
• Providing support to stakeholders on data related deliverables using in-house analytics and reporting tools.

HSE Responsibilities:

• Leading HSE efforts according to ISO 45001 and Bayer safety standards focusing on legal compliance
• and identifying safety risks applying the HIRA methodology.
• Adherence to all applicable legislation, Bayer safety policies and procedures.
• Actively promoting the Bayer safety culture and best practices.
• Participation in compiling Risk assessments and Job safety analysis.
• Actively recording safety observation and near misses.
• Participating in 5WHY problem solving identifying corrective and preventative actions.

Experience, Skills and Qualifications

• BSc or MSc in Computer Science, Data Science, Mathematics, Statistics, Biological Sciences, Computational Biology, Agriculture or similar.
• 2-years’ experience in a relevant Agricultural discipline (agricultural research, plant physiology, plant breeding or related field).
• Strong programming skills in R, Python, SQL.
• Strong aptitude for statistics.
• Experience with visualization software such as Tableau and TIBCO Spotfire.
• Proficiency in Machine Learning and Statistics algorithms and concepts (Mixed Models, Random Forest, Deep Learning, SVM, SAS JMP etc.).
• Experience with data mining and statistical techniques (Logistic Regression, Linear Regression, Decision Trees, etc.).
• Problem solving ability and experience in analyzing and presenting complex data.
• Organizational, interpersonal, and written communication skills.