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.
