Working time: Monday- Saturday.
Working location:
Working in Dong Nai R&D Center 1- 2 days/ week, Having shuttle bus from Sala.
Working mainly in Sala office (4 days/ week)
YOUR MISSION:
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.
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.
KEY ACTIVITIES:
Data System Development & Maintenance
Automate data synchronization and reporting wherever possible (e.g., via Power Query, SQL, or Python/R scripts).
Standardize data structures, naming conventions, and version control across teams.
Build and maintain centralized databases and data pipelines covering all R&D experiments (Genetics, Feed Formulation, and Production trials).
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.
Data Integrity & Quality Control
Coordinate with experiment leads to resolve data discrepancies and close quality gaps.
Conduct systematic data audits and integrity checks before analysis or reporting.
Maintain traceable records of every dataset version, transformation, and correction.
Establish and enforce data governance standards to ensure accuracy, completeness, and consistency across all R&D datasets.
Implement automated validation and error- flagging tools to detect anomalies early.
Data Analysis & Reporting
Produce comparative summaries and visualizations that translate raw data into clear biological or operational insights.
Apply suitable statistical approaches (e.g., ANOVA, regression, mixed- effects models) to evaluate treatment effects and validate experimental results.
Collaborate with scientists to interpret results, highlight key findings, and recommend evidence- based actions for R&D optimization.
Test assumptions, analyze variance structures, and confirm model fit for scientific rigor.
Develop reproducible analytical workflows or scripts (R, Python, or Excel) to standardize routine analyses.
Cross- functional Collaboration & Support
Contribute to planning and design of experiments by advising on data structure and statistical approaches.
Serve as the main data liaison between Genetics, Feed R&D, and Operations teams.
Support project leads with data- driven summaries for management reviews.
