scientist岗位职责

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, commercialization of innovative drugs. You will work collaboratively with cross-functional teams to inform strategic decision-making enhance patient outcomes. Key Responsibilities: • Data Acquisition, Analysis Modeling: o Collaborate with internal external partners to acquire, validate, integrate real-world data from diverse sources. Ensure data quality integrity, maintain comprehensive documentation freproducibility o Design implement advanced statistical models machine learning algorithms to extract insights from various real-world data sources, including electronic health records (EHRs), claims data, patient registries, more. o Analyze complex datasets to identify trends, patterns, opportunities that can inform product development market strategies. • Support Real-World Studies/Health Outcomes Research: o Design execute real-world studies to generate real-world evidence, supporting the safety, efficacy, economic value of products. o Contribute to study design, including defining objectives, selecting appropriate methodologies, developing analytical plans. o Oversee perform data curation cleaning processes to ensure high-quality datasets fanalysis. o Collaborate with cross-functional teams to ensure the alignment of study objectives with business regulatory goals. • Insight Generation: o Translate complex analytical results actionable insights communicate findings to stakeholders, including research scientists, clinical teams, commercial partners. o Develop data visualizations reports that convey the impact of real-world data on clinical business decisions. • Innovation Improvement: o Stay updated with industry trends innovations in data science, machine learning, real-world evidence. o Propose develop new methodologies to improve data processing analysis capabilities. • Cross-Functional Collaboration: o Work closely with clinical, regulatory, commercial, technology teams to ensure alignment integration of data-driven insights business processes. Qualifications: • Education: o Master’s Ph.D. degree in Data Science, Biostatistics, Epidemiology, Health Outcomes, a related field. • Experience: o Proven experience (3+ years) in data analysis, preferably in the pharmaceutical healthcare industry. o Experience working with real-world data sources, such as EHRs, claims data, patient registries. • Technical Skills: o Proficiency in statistical data analysis software such as SQL, R, SAS, Python, similar. o Experience with data visualization tools like Tableau, Power BI, similar. o Familiarity with machine learning frameworks techniques. • Soft Skills: o Strong analytical problem-solving skills. o Excellent communication 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 regulatory environments. • Knowledge of healthcare systems, epidemiology, outcomes research. Other requirement • May need travel on-site data curation/analysis as required: o Engage with local vendto understdata sources ensure proper data collection methods. o Conduct on-site data curation analysis to ensure immediate 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。
【工作内容】负责从电子健康记录、索赔数据等现实世界数据源获取、验证、整合数据，设计并实施统计模型和机器学习算法；支持现实世界研究设计与执行，生成真实世界证据；将分析结果转化为可行动洞见，制作数据可视化报告；持续关注行业创新，提出新方法；与跨职能团队协作，确保数据驱动决策。
【基本条件】需持有数据科学、生物统计学、流行病学或相关领域的硕士或博士学位；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；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；具备出色的分析与问题解决能力、优秀的沟通与演示能力；身体素质：不限，健康程度：不限

英语读写熟练 CET-4/6，口语流利加分；
要Python或Data Scientist
Front End: React Native; React JS
Back End: Python
Fullstack: React Native/React JS+Python
RAG/向量库

The role
Siemens builds the systems the physical world runs on: factories, power grids, buildings, trains, hospitals. Industrial physical AI is a majopportunity in applied AI, one of the harder ones to get right. There is a generation of AI-powered products to build.
We are forming an applied scienceganization in China to build the science behind them. The work sits at the intersection of machine learning research, real world data, production systems running in industrial environments.
As Principal Applied Scientist, you lead the science on a majcapability area inside the pod. You take ambiguous problems through hypothesis, method selection, experimentation, models that run in production. You are hands on with the modeling, you are accountable fthe rigthe outcome.
This is a seniindividual contributrole. You will work in close partnership with the SeniPrincipal Applied Scientist, who sets the scientific direction fthe pod, with engineers product managers who turn your work product.

Key responsibilities
Own one more scientific capability areas end to end, fexample perception, language agents, time series, control, planning, evaluation.
Take problems from ambiguous product system requirements through clear research questions, hypotheses, success metrics.
Lead applied research projects: literature review, method selection, experimentation, ablation, erranalysis, productization.
Build run the evaluation pipelines fthe work you own: offline metrics, online experiments, robustness testing in industrial conditions.
Work with engineers to take models production grade pipelines: data readiness, training infrastructure, inference, observability.
Make scientific tradeoffs in front of engineers product managers, with evidence, translate them decisions the team can act on.
Identify de-risk scaling challenges in your area: data quality, model drift, latency, throughput, cost, safety.
Raise the bar on experimentation rigor, reproducibility, documentation across the team.
Apply responsible AI practices in your work: bias detection, model risk management, human in the loop controls.

Basic qualifications
8+ years in applied machine learning, AI research, data science, with models that shipped to production made an impact.
Strong foundation in machine learning theory practice across training, evaluation, deployment.
Demonstrated experience taking research from a paper prototype a model that runs reliably in production.
Proficiency in Python modern ML frameworks toolchains.
Strong partnership track record with engineering teams on data, training infrastructure, inference.
Clear written verbal communication with engineers, product managers, senileaders.

Preferred qualifications
Experience applying ML in industrial physical domains: manufacturing, automation, robotics, energy, mobility, infrastructure, healthcare.
Deep expertise in one of: multimodal ML, generative AI, retrieval augmented generation, agentic workflows, time series, control, planning.
Hands-on experience building evaluation pipelines, running online experiments, instrumenting production monitoring fa model you owned.
Publications, patents, open source contributions, significant internal technology transfers.
Experience mentoring more juniscientists engineers.
Experience working with globally distributed research, product, engineeringganizations.