Laboratory Data Analyst – APAC
Key Responsibilities:
Data Collection and Analysis:
Gather, analyze and validate laboratory data from various sources, including test results, instrument data, and quality control records.
Quality Assessment:
Evaluate data quality, identify potential issues, and implement corrective actions to ensure accuracy and reliability.
Trend Identification:
Analyze data to identify trends, patterns, or issues that could influence experimental outcomes and identify potential areas for improvement in laboratory processes.
Reporting and Communication:
Prepare reports and presentations summarizing data analysis findings and recommendations for improvement.
Process Improvement:
Collaborate with Managers to implement process improvements based on data analysis findings.
Compliance:
Ensure adherence to relevant regulations and industry standards.
Equipment Maintenance:
Assist with instrument calibration and verification.
Training:
Provide training to laboratory staff on data analysis and quality control procedures.
Special Projects:
May be involved in special projects related to quality.
Other ad-hoc duties as assigned by the Management.
Requirements:
- Bachelor’s degree in a relevant field (biology, chemistry, chemical engineering, data science, or related field).
- Min 5 years industry experience.
- Able to collect, organize, interpret, and present data using various techniques including descriptive statistics, inferential statistics, hypothesis testing, or data visualization.
- Familiar with the principles and practices of quality management like the Plan-Do-Check-Act cycle and the Six Sigma methodology.
- Able to identify and solve quality problems using logical and creative thinking.
- Able to communicate clearly and effectively with different stakeholders using verbal, written, or visual means.
- Proficient in using various tools and software to support data analysis and quality management task.
- Strong attention to detail and ability to spot data inconsistencies and errors.
- Ability to adapt to new technologies, methodologies and challenges.
