QC实验室Data Scientist

工作描述JOB DESCRIPTION

1. 负责构建面向QC实验室的数据科学能力，基于实验室电子化和数字化需求，开展数据识别、数据源匹配、数据治理、数据分析和数据产品设计，支持检验效率提升、数据完整性、合规性和持续改进。

Build data science capabilities for QC laboratories by identifying digitalization needs, matching appropriate data sources, performing data governance, analytics and data product design to improve testing efficiency, data integrity, compliance and continuous improvement.

2. 熟悉并应用QC实验室关键系统及数据结构，包括LIMS、LES、ELN/E-logbook、Empower/CDS、LabX、Maximo、EMS等，能够根据业务场景精准定位数据来源和数据口径。

Understand and apply key QC laboratory systems and data structures, including LIMS, LES, ELN/E-logbook, Empower/CDS, LabX, Maximo and EMS, and accurately identify data sources and data definitions based on business scenarios.

3. 负责电子化项目或数据分析项目的需求澄清、数据流程梳理、字段映射、数据质量评估、数据清洗、模型或规则设计、验证支持、上线部署和生命周期维护。

Own requirement clarification, data process mapping, field mapping, data quality assessment, data cleansing, model or rule design, validation support, deployment and lifecycle maintenance for digitalization or data analytics projects.

4. 具备数据库基础知识，能够理解关系型数据库、数据表、主键、字段、数据字典、接口数据及常见查询逻辑；支持基于SQL或同类工具进行数据抽取、核对、追溯和分析。

Understand database fundamentals, including relational databases, tables, primary keys, fields, data dictionaries, interface data and common query logic; support data extraction, reconciliation, traceability and analysis using SQL or similar tools.

5. 与QC、QA、IT、自动化、数据工程及业务专家合作，建立从业务问题、电子化需求、系统数据源、数据加工逻辑到分析结果的端到端数据解决方案。

Collaborate with QC, QA, IT, automation, data engineering and business SMEs to build end-to-end data solutions from business questions and digitalization requirements to system data sources, data processing logic and analytical outputs.

6. 支持多源实验室数据的清洗、标准化、整合、主数据管理、数据血缘梳理和可视化，包括检验结果、样品、批次、方法、仪器、试剂耗材、稳定性、环境监测及审计追踪等数据。

Support cleaning, standardization, integration, master data management, data lineage mapping and visualization of multi-source laboratory data, including test results, samples, batches, methods, instruments, reagents/consumables, stability, environmental monitoring and audit trail data.

7. 在GMP和数据完整性要求下，参与数据治理框架、数据标准、权限管理、审计追踪、变更控制、验证策略及相关SOP/技术文件的制定、执行或审核。

Participate in the development, execution or review of data governance frameworks, data standards, access control, audit trail, change control, validation strategy and related SOPs/technical documents under GMP and data integrity requirements.

8. 设计并交付QC实验室数据分析与数据产品，包括检验趋势分析、OOS/OOT辅助分析、稳定性趋势、仪器利用率、样品流转、审计追踪复核、电子记录查询和自动化报告等。

Design and deliver QC laboratory analytics and data products, including test trend analysis, OOS/OOT assisted analysis, stability trending, instrument utilization, sample flow tracking, audit trail review, electronic record search and automated reporting.

9. 支持公司内外部客户审计及法规审计中与电子化系统、数据治理、数据完整性、数据追溯和计算机化系统相关的准备、说明和整改工作。

Support internal/external client audits and regulatory inspections related to digital systems, data governance, data integrity, data traceability and computerized systems.

10. 推动数据科学和电子化相关持续改进项目，评估数据产品的准确性、可用性、用户采纳率、效率提升和风险控制情况，并形成可复用的数据治理和分析最佳实践。

Drive continuous improvement projects related to data science and digitalization, evaluate the accuracy, usability, user adoption, efficiency improvement and risk control of data products, and establish reusable best practices for data governance and analytics.

11. 完成主管指派的其他相关工作。

Perform any other duties assigned by the supervisor.

人员资质 PERSONAL QUALIFICATION

1. 本科及以上学历，数据科学、统计学、计算机、软件工程、自动化、生物医学工程、药学、化学、生物化学或相关专业背景。

Bachelor’s degree or above in Data Science, Statistics, Computer Science, Software Engineering, Automation, Biomedical Engineering, Pharmacy, Chemistry, Biochemistry or related disciplines.

2. 具有3-5年数据分析、数据治理、实验室数字化、计算机化系统、数据库或数据产品相关经验；具备QC实验室、GMP或制药行业经验者优先。

3-5 years of experience in data analytics, data governance, laboratory digitalization, computerized systems, databases or data products; QC laboratory, GMP or pharmaceutical industry experience is preferred.

3. 熟悉LIMS、LES、ELN/E-logbook、Empower/CDS等QC实验室电子化系统及其典型业务流程、数据对象和数据结构。

Familiar with QC laboratory digital systems such as LIMS, LES, ELN/E-logbook and Empower/CDS, including typical business processes, data objects and data structures; able to understand relationships among samples, batches, methods, tests, results, instruments and audit trails, and support data source matching and field definition confirmation.

4. 具备数据库基础和数据建模意识，了解关系型数据库、数据表设计、主键/外键、字段映射、数据字典、接口数据、数据血缘和常见数据质量问题。

Possess basic database knowledge and data modeling awareness, including relational databases, table design, primary/foreign keys, field mapping, data dictionaries, interface data, data lineage and common data quality issues.

5. 熟悉SQL或同类查询工具，具备数据抽取、清洗、核对、统计分析、可视化看板或自动化报表经验；熟悉Power BI或类似工具者优先。

Familiar with SQL or similar query tools, with experience in data extraction, cleansing, reconciliation, statistical analysis, visualization dashboards or automated reporting; experience with Power BI or similar tools is preferred.

6. 了解GMP、数据完整性、计算机化系统验证、21 CFR Part 11、EU GMP Annex 11等法规或指南要求，能够在合规框架下开展数据治理和数据分析工作。

Knowledge of GMP, data integrity, computerized system validation, 21 CFR Part 11, EU GMP Annex 11 and related regulatory or guidance requirements, with the ability to perform data governance and analytics within a compliance framework.

7. 具备良好的业务理解、逻辑分析、问题拆解、跨部门沟通和项目推进能力；能够将实验室电子化需求转化为清晰的数据需求、数据规则和交付方案，自我驱动，学习能力强，结果导向，并具备基本英语沟通能力。

Strong business understanding, logical thinking, problem decomposition, cross-functional communication and project execution skills; able to translate laboratory digitalization needs into clear data requirements, data rules and delivery plans; self-driven, fast learner, result-oriented and able to communicate in basic English.