Data Scientist - Bioprocess Analytics, PAT & Digital Twin

1. 01

   Bioprocess Development Data Analysis Integrate and manage experimental and manufacturing datasets generated during cell culture process development. Perform data cleaning, exploratory data analysis (EDA), data quality assessment, and statistical analysis. Analyze relationships between process parameters and process outcomes across different projects. Identify trends, sources of variability, and potential drivers impacting process performance and product quality.

2. 02

   Process Modeling and Advanced Analytics Develop predictive models using PAT (Process Analytical Technology) data and offline analytical measurements to enable real-time prediction of critical process indicators. Apply and evaluate a variety of statistical, machine learning, and multivariate analysis methods for bioprocess applications. Utilize multivariate process analysis (MPA/MVA) techniques to investigate relationships among process parameters, PAT measurements, and process outcomes. Identify Critical Process Parameters (CPPs) and assess their impact on Critical Quality Attributes (CQAs). Support digital twin, soft-sensor, and advanced process monitoring initiatives.

3. 03

   Model Evaluation and Validation Contribute to the establishment of standardized model evaluation and validation frameworks. Assess model robustness, generalization capability, prediction accuracy, and lifecycle performance. Develop reusable data analysis workflows, modeling pipelines, and computational tools. Participate in model

and deployment activities for regulated GMP environments. 4. Data Automation and Digital Tool Development Develop automated data processing and analysis scripts to improve efficiency and scalability. Build visualization dashboards and analytical applications for process monitoring and decision support. Support the development of enterprise digital platforms for bioprocess analytics and PAT deployment. Technical Skills Strong foundation in statistics and data science, with hands-on experience in: Regression analysis Multivariate statistical analysis Feature engineering Time-series analysis Experimental data analysis Proficiency in at least one programming language for data analysis and model development, such as: Python R MATLAB Experience with machine learning and/or deep learning techniques, including: Model development Hyperparameter optimization Model validation Performance evaluation Familiarity with automated data analytics workflows and software development practices. Experience developing visualization dashboards and analytical applications using tools such as: Power BI Tableau Plotly Dash Streamlit Spotfire Soft Skills Strong communication and stakeholder engagement skills. Ability to translate scientific or process development challenges into data-driven analytical and modeling solutions. Strong problem-solving and critical thinking capabilities. Passion for continuous learning and the ability to rapidly acquire knowledge in emerging technologies and biopharmaceutical applications. Fluent in both Chinese and English, with the ability to participate in technical discussions and project communications in English. Education Master's degree or above in Statistics, Mathematics, Data Science, Computer Science, Bioinformatics, Chemical Engineering, Bioprocess Engineering, or related fields. Ph.D. candidates are highly encouraged to apply.