MSAT Data Scientist Full-time

Data Management Specialist

Major responsibilities

1. Data Management: Responsible for the collection, sorting and integration of all production data, including raw material inspection, process control, finished product testing, environmental monitoring, change control and other quality data. Establishing a unified data model and data quality monitoring to meet ALCOA+ requirements.
2. Establish a production/quality process visualization and monitoring system, conduct in-depth data analysis of processes and quality, design and maintain SPC charts, control limits, CP/CPK indicators and alarm rules, conduct trend analysis, deviation identification and process capability assessment, accurately locate process and quality operation problems and fluctuation trends, and provide decision support for core tasks such as process robustness assessment, process capability monitoring, process optimization, process validation, comparability studies, quality improvement and anomaly investigation.
3. Visualization and Reporting: Build visual dashboards for production and quality data, monitor key indicators of production operations and quality control in real time, and clearly communicate insights and recommendations to production, quality, and management.
4. Cross-departmental collaboration: Working with production, QA/QC, engineering, IT, and other departments to promote the implementation and large-scale reuse of data projects.
5. Proficient in using various AI tools to build data processing models adapted to biopharmaceutical production scenarios. Optimize data processing and analysis processes. Improve data processing efficiency and analytical accuracy and achieve intelligent analysis and application of data.
6. Technology Optimization and Methodological Iteration: Continuously optimize data analysis methods and data governance workflows, keep up with advanced data science technologies and analytical tools in industry. Improve the efficiency and accuracy of data analysis and facilitate the digital upgrade of production processes.
7. AI and data science empowering manufacturing: Through AI and data-driven insights, it helps the company achieve digital and refined management of production, providing decision support for quality compliance, cost optimization, capacity and efficiency improvement.

Education, Qualifications, Skills and Experience

• Master's degree or above in biopharmaceutical engineering, bioengineering, statistics, data science, applied mathematics, pharmaceutical engineering, or related fields. Candidates with a combined background in biopharmaceutical process and data science are preferred.
• 6 years or more of experience in the biopharmaceutical industry as an MSAT, process data analyst, or data scientist.
• Familiarity with biopharmaceutical production processes; practical experience in process data governance, statistical analysis, and process validation projects is preferred.
• Proficient in production data statistical analysis methods, with a thorough understanding of SPC, CPK, comparability analysis, and process validation-related data analysis logic and practical skills.
• Proficient in using data processing tools such as SQL and Python/R, and skilled in process statistical analysis software such as JMP, Minitab, and SPSS.
• Able to skillfully utilize AI tools for data processing, model building, and optimization, those with practical experience in AI data analysis models are preferred.
• Possesses rigorous logical thinking, data sensitivity, and problem-solving abilities.
• Strong written English and fluent oral English.

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.