R&D Data Executive

Working time: Monday - Saturday.

Working location:

• Working mainly in Sala office (4 days/ week)
• Working in Dong Nai R&D Center 1-2 days/ week, Having shuttle bus from Sala.

YOUR MISSION:

• To build and maintain a unified, reliable, and efficient data system for the entire R&D department.
• You will design data collection SOPs, manage data integrity across Genetics and Feed Formulation projects, and deliver dashboards and descriptive analyses that provide actionable insights.
• Your mission is to ensure that R&D decisions are grounded in accurate, traceable, and well-structured data, enabling a data-driven culture that supports continuous improvement and innovation.

KEY ACTIVITIES:

1. Data System Development & Maintenance

• Build and maintain centralized databases and data pipelines covering all R&D experiments (Genetics, Feed Formulation, and Production trials).
• Standardize data structures, naming conventions, and version control across teams.
• Automate data synchronization and reporting wherever possible (e.g., via Power Query, SQL, or Python/R scripts).

2. SOP Design & Implementation

• Develop, document, and continuously improve SOPs for data collection, validation, and transfer.
• Train team members on proper data entry, file organization, and metadata recording.
• Monitor compliance with SOPs and coordinate corrective actions when inconsistencies arise.

3. Data Integrity & Quality Control

• Establish and enforce data governance standards to ensure accuracy, completeness, and consistency across all R&D datasets.
• Conduct systematic data audits and integrity checks before analysis or reporting.
• Implement automated validation and error-flagging tools to detect anomalies early.
• Maintain traceable records of every dataset version, transformation, and correction.
• Coordinate with experiment leads to resolve data discrepancies and close quality gaps.

4. Data Analysis & Reporting

• Apply suitable statistical approaches (e.g., ANOVA, regression, mixed-effects models) to evaluate treatment effects and validate experimental results.
• Test assumptions, analyze variance structures, and confirm model fit for scientific rigor.
• Produce comparative summaries and visualizations that translate raw data into clear biological or operational insights.
• Develop reproducible analytical workflows or scripts (R, Python, or Excel) to standardize routine analyses.
• Collaborate with scientists to interpret results, highlight key findings, and recommend evidence-based actions for R&D optimization.

5. Cross-functional Collaboration & Support

• Serve as the main data liaison between Genetics, Feed R&D, and Operations teams.
• Support project leads with data-driven summaries for management reviews.
• Contribute to planning and design of experiments by advising on data structure and statistical approaches.