资深统计学家(RWE)
Principal Statistician (RWE)
职位目的
首席统计学家的职责是为Veramed在多个项目、客户和治疗领域中的真实世界证据（RWE）的统计部分提供领导力和专业知识。
主要职责
首席统计学家（RWE）将负责制定统计分析计划，对方案提供重要输入，并领导使用主要来自电子健康记录（EHRs）和医疗索赔数据集的匿名患者级数据进行的RWE研究分析。他们应对RWE数据的优势和挑战有深入理解，特别关注如何以及是否可以得出因果结论。
首席统计学家（RWE）需要既能带领项目工作，也能独立工作（包括对其交付成果和中间输出进行质量检查）。此外，该职位还需要审查其他同事的成果，通常是那些资历较浅的同事，并在发现任何错误（无论是偶然还是系统性的）时提供流程改进的建议。
**技术方面**
• 确定并使用最合适的统计分析技术，包括假设检验、逻辑回归和线性回归、混合模型、处理缺失数据的模式混合模型、用于测量缺失性的正式统计技术（MCAR、MNAR等）以及倾向得分方法（匹配、加权）
• 进行生存分析，如Kaplan-Meier曲线、Cox比例风险模型
• 进行元分析技术，包括文献综述和网络元分析
• 提供从EHRs、索赔数据和患者登记数据库（美国/欧洲/日本等）实际操作中获得的专业知识
• 提供数据隐私、小样本规则和医疗系统背景方面的技能
• 执行数据管理活动，例如：大型数据集处理：清理、整理、合并和填补缺失数据的过程文档
• 在分析数据集中设计派生变量
**通用职责**
• 有效参与内部和客户研究团队会议，包括在客户会议上汇报研究进展
• 与团队成员和同事分享科学、技术和实践知识
• 与内部和客户团队成员建立有效的协作关系
• 寻找机会进行跨部门合作
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Purpose
The role of the Principal Statistician is to provide leadership and expertise in the statistical components of real-world evidence (RWE) to Veramed’s clients across a range of projects, clients and therapeutic areas.
Key Responsibilities
The Principal Statistician (RWE) will be responsible for taking the lead on the development of statistical analysis plans, providing significant input into protocols and leading analyses of RWE studies that employ anonymised patient-level data derived predominantly from electronic health records (EHRs) and medical claims datasets. They will have a deep understanding of both the benefits and challenges of using RWE data, with a particular focus on how and whether causal conclusions can be drawn.
The Principal Statistician (RWE) is expected to both lead work projects but also to work independently (including carrying out QC checks on their own deliverables and interim outputs). The Principal Statistician (RWE) will, additionally, be expected to review outputs from other colleagues, typically those more junior to them, and to provide advice on process improvements if any errors (whether one-off or systematic) are found in other peoples’ work.
**Technical**
• Determine and use the most appropriate statistical analysis techniques including hypothesis testing, logistic & linear regression, mixed models, pattern mixture models for handling missing data, formal statistical techniques for measuring missingness (MCAR, MNAR etc) and propensity score methods (matching, weighting)
• Conduct survival analysis such as Kaplan-Meier curves, Cox proportional hazards models
• Conduct meta-analysis techniques including literature reviews and network meta-analysis.
• Provide expertise gained from hands-on experience with EHRs, claims data and patient registries (US/European/Japanese etc)
• Provide skills in data privacy, small number rules, and healthcare system context
• Carry out data management activities such as: Large dataset handling: documentation of processes for cleaning, wrangling, merging, and imputing missing data
• Designing derived variables within the analytic dataset
**General**
• Effectively participate in internal and client study team meetings including presenting study updates in client meetings
• Share scientific, technical and practical knowledge within the team and with colleagues
• Build effective collaborative working relationships with internal and client team members
• Seek opportunities to develop innovative ideas, sharing when appropriate
• Lead creation of, and implement internal training programmes (including materials)
• Drive internal and client process improvement initiatives including determining and resolving any systematic cause of errors
• Be aware of all the latest developments in primary field of expertise and disseminate as appropriate • Perform work in full compliance with applicable legislation, regulations, company policies, procedures and training
**People Management**
• Line management of statisticians and other technical staff. Accountable for overall performance of direct reports
• Provide coaching and mentoring of staff to achieve “excellence”.
• Direct employee career development and ensure line reports receive appropriate training to perform their day-to-day jobs
• Interview and effectively on-board and integrate new staff members
• Provide statistical technical leadership and coaching
Minimum Qualification Requirements
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Physics or other subject with high statistical content
• Master’s degree in statistics (biostatistics preferred)
• Minimum 8 years of relevant industry experience
• Excellent R programming skills and the ability to implement complex logic
Other Information/Additional Requirements
• Pharmaceutical industry experience
• Training or experience in epidemiological methods
• Comfortable creating complex analysis data sets derived from various data sources
• Possess excellent skills in R or SAS programming; good appreciation of SQL programming
• Be able to employ a range of techniques to address the issue of confounding in causal or comparative RWE studies
• Familiar with FDA, EMA, MHRA guidelines and ENCEPP outputs • Be familiar with common data model structures
• Possess strong communication, time management and documentation skills
• Has a careful eye for outliers and errors
**_Veramed is a B Corp_** _accredited company which means that we use the power of business to build a more inclusive and sustainable economy meeting the highest verified standards of social and environmental performance, transparency, and accountability._
_As an organisation that has people at the heart of it, Veramed is committed to creating a diverse environment and is proud to be an equal opportunities employer. We foster a working culture where employees have integrity, honesty and respect for one another without regard to race, national origin, religion, gender identity or expression, sexual orientation or disability. All qualified applicants will receive equal consideration for employment._