R&D Data Executive

Mô tả Công việc
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.