真实世界数据科学家（Real-World Data Scientist ，全球前十外资药企））

As a Real-World Data Scientist , you will play a critical role in harnessing real-world data to generate insights that support the development, approval, and commercialization of innovative drugs. You will work collaboratively with cross-functional teams to inform strategic decision-making and enhance patient outcomes.
Key Responsibilities:
• Data Acquisition, Analysis and Modeling:
o Collaborate with internal and external partners to acquire, validate, and integrate real-world data from diverse sources. Ensure data quality and integrity, and maintain comprehensive documentation for reproducibility
o Design and implement advanced statistical models and machine learning algorithms to extract insights from various real-world data sources, including electronic health records (EHRs), claims data, patient registries, and more.
o Analyze complex datasets to identify trends, patterns, and opportunities that can inform product development and market strategies.
• Support Real-World Studies/Health Outcomes Research:
o Design and execute real-world studies to generate real-world evidence, supporting the safety, efficacy, and economic value of products.
o Contribute to study design, including defining objectives, selecting appropriate methodologies, and developing analytical plans.
o Oversee and perform data curation and cleaning processes to ensure high-quality datasets for analysis.
o Collaborate with cross-functional teams to ensure the alignment of study objectives with business and regulatory goals.
• Insight Generation:
o Translate complex analytical results into actionable insights and communicate findings to stakeholders, including research scientists, clinical teams, and commercial partners.
o Develop data visualizations and reports that convey the impact of real-world data on clinical and business decisions.
• Innovation and Improvement:
o Stay updated with industry trends and innovations in data science, machine learning, and real-world evidence.
o Propose and develop new methodologies to improve data processing and analysis capabilities.
• Cross-Functional Collaboration:
o Work closely with clinical, regulatory, commercial, and technology teams to ensure alignment and integration of data-driven insights into business processes.
Qualifications:
• Education:
o Master’s or Ph.D. degree in Data Science, Biostatistics, Epidemiology, Health Outcomes, or a related field.
• Experience:
o Proven experience (3+ years) in data analysis, preferably in the pharmaceutical or healthcare industry.
o Experience working with real-world data sources, such as EHRs, claims data, or patient registries.
• Technical Skills:
o Proficiency in statistical and data analysis software such as SQL, R, SAS, Python, or similar.
o Experience with data visualization tools like Tableau, Power BI, or similar.
o Familiarity with machine learning frameworks and techniques.
• Soft Skills:
o Strong analytical and problem-solving skills.
o Excellent communication and presentation abilities to convey complex information to non-technical stakeholders.
o Ability to work effectively in a team-oriented, collaborative environment.
Preferred Qualifications:
• Experience in drug development processes or regulatory environments.
• Knowledge of healthcare systems, epidemiology, and outcomes research.
Other requirement
• May need travel and on-site data curation/analysis as required:
o Engage with local vendor to understand data sources and ensure proper data collection methods.
o Conduct on-site data curation and analysis to ensure immediate and accurate data handling, facilitating timely insights.
该岗位核心是利用现实世界数据（Real-World Data, RWD）生成洞见，为创新药物的研发、审批及商业化提供支持，同时需与跨职能团队协作，助力战略决策并改善患者预后。
一、核心职责
1. 数据获取、分析与建模
• 与内外部合作伙伴协作，获取、验证并整合来自不同来源的现实世界数据，确保数据质量与完整性，并维护详尽文档以保障可重复性。
• 设计并实施先进的统计模型与机器学习算法，从电子健康记录（EHRs）、索赔数据、患者登记系统等各类现实世界数据源中提取洞见。
• 分析复杂数据集，识别可为产品研发和市场策略提供参考的趋势、模式与机遇。
2. 支持现实世界研究/健康结局研究
• 设计并执行现实世界研究以生成现实世界证据（Real-World Evidence, RWE），支撑产品的安全性、有效性与经济学价值。
• 参与研究设计，包括明确研究目标、选择合适的方法学及制定分析计划。
• 监督并执行数据整理与清洗流程，确保用于分析的数据集质量达标。
• 与跨职能团队协作，确保研究目标与业务及监管目标保持一致。
3. 洞见生成
• 将复杂的分析结果转化为可落地的洞见，并向研究科学家、临床团队、商业合作伙伴等利益相关者传达研究发现。
• 制作数据可视化内容与报告，阐明现实世界数据对临床及业务决策的影响。
4. 创新与改进
• 持续关注数据科学、机器学习及现实世界证据领域的行业趋势与创新动态。
• 提出并开发新方法，以提升数据处理与分析能力。
5. 跨职能协作
• 与临床、监管、商业及技术团队紧密合作，确保数据驱动的洞见能融入业务流程并保持方向一致。
二、任职要求
1. 教育背景
• 需持有数据科学、生物统计学、流行病学、健康结局或相关领域的硕士或博士学位。
2. 工作经验
• 具备3年及以上数据分析经验，有制药或医疗健康行业经验者优先。
• 拥有处理电子健康记录（EHRs）、索赔数据或患者登记系统等现实世界数据源的经验。
3. 技术技能
• 熟练使用SQL、R、SAS、Python等统计与数据分析软件。
• 掌握Tableau、Power BI等数据可视化工具的使用。
• 熟悉机器学习框架与相关技术。
4. 软技能
• 具备出色的分析与问题解决能力。
• 拥有优秀的沟通与演示能力，能向非技术背景的利益相关者传递复杂信息。
• 能在团队导向的协作环境中高效工作。
三、优先考虑条件
• 具备药物研发流程或监管环境相关经验。
• 了解医疗健康系统、流行病学及结局研究知识。
四、其他要求
• 可能需根据工作需求出差并进行现场数据整理/分析：
◦ 与当地供应商对接，了解数据源并确保数据收集方法合规。
◦ 开展现场数据整理与分析工作，保障数据处理的即时性与准确性，助力及时生成洞见。