高级临床信息学家
Senior Clinical Informaticist
Verantos (https://verantos.com) 是生命科学领域高有效性真实世界证据（RWE）的全球领导者。通过整合强大的临床叙事数据、人工智能（AI）技术和测量有效性，Verantos 是首家在治疗领域大规模生成研究级证据的公司。
Verantos 证据平台整合异构的真实世界数据源，并生成满足市场准入、卫生经济学和结果研究（HEOR）、医学事务和监管使用的准确证据。利用数据科学、AI 和先进的数据来源如电子健康记录（EHR），该平台支持多个治疗领域的复杂临床研究。如今，全球一些最大的生物制药公司都是 Verantos 的客户。
作为高级临床信息学家，您将通过领导知识管理、语义标准化和数据质量方面的努力，帮助塑造临床数据如何被转化并用于研究。您将与临床医生、科学家、数据科学家、产品经理和工程师合作，定义映射临床概念的可扩展方法，识别关键概念的采集位置和方式，开发用于队列创建的概念列表，并设计临床数据质量检查以确保数据集符合临床期望。
您的工作将直接支持高影响力的研究，确保正确的概念被捕捉、标准化并在多种数据源中可用。这是一个在临床洞察和技术实现交汇点工作的机会，将复杂的医疗数据转化为推动大规模决策的高质量证据。
职责
知识管理
- 创建和维护概念组。
- 审查客户代码列表并根据需要推荐更新。
- 列出并定义每个实用注册表中的关键变量。
语义标准化
- 将理赔数据和医疗系统数据映射到 OMOP 公共数据模型（CDM）中的标准概念。
- 对来自多个不同医疗系统的 CDM 转换数据进行语义标准化，并映射高优先级的未映射概念。
数据可用性
- 创建最佳使用数据生成见解的文档。
- 与客户合作，帮助他们理解如何利用数据回答研究问题。
数据质量
- 定义特定疾病领域的数据质量测试流程。
概念 ID
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Overview
Verantos (https://verantos.com) is the global leader in high-validity real-world evidence (RWE) for life sciences. By incorporating robust clinical narrative data, artificial intelligence (AI) technology, and measured validity, Verantos is the first company to generate research-grade evidence at scale across therapeutic areas.
The Verantos Evidence Platform integrates heterogeneous real-world data sources and generates evidence with the accuracy required for market access, health economics and outcomes research (HEOR), medical affairs, and regulatory use. Leveraging data science, AI, and advanced data sources such as electronic health records (EHRs), the platform supports complex clinical studies across multiple therapeutic areas. Today, some of the largest biopharma companies in the world are Verantos customers.
As a Senior Clinical Informaticist, you will help shape how clinical data is transformed and made usable for research by leading efforts in knowledge management, semantic normalization, and data quality. You will collaborate with clinicians, scientists, data scientists, product managers, and engineers to define scalable approaches for mapping clinical concepts, identify where and how key concepts can be captured, develop concept lists for cohort creation, and design clinical data quality checks to ensure datasets meet clinical expectations.
Your work will directly support high-impact research by ensuring that the right concepts are captured, standardized, and accessible across diverse data sources. This is an opportunity to work at the intersection of clinical insight and technical implementation, transforming complex healthcare data into research-grade evidence that advances decision-making at scale.
Responsibilities
Knowledge management
- Create and maintain concept groups.
- Review customer code lists and recommend updates as needed.
- List and define critical variables for each pragmatic registry.
Semantic normalization
- Map claims data and health system data to standard concepts in the OMOP Common Data Model (CDM).
- Semantically normalize CDM-converted data coming from multiple different health systems and map high priority unmapped concepts
Data usability
- Create documentation on how to best use data to generate insights.
- Collaborate with customers to help them understand how to leverage data to answer research questions.
Data Quality
- Define disease-area–specific data quality testing processes.
Concept Identification
- Determine how to best capture concepts needed to answer customer research questions and analyze prevalence of concepts for feasibility assessments.
- Specify annotation projects to capture complex clinical ideas.
Qualifications
Required
- MD or DO degree required; completion of an accredited residency program strongly preferred.
- Minimum of 2 years of clinical experience required.
- Experience working with standard clinical vocabularies such as SNOMED CT, LOINC, RxNorm, ICD-10-CM, CPT, or HCPCS, including an understanding of their structure and use in representing clinical data.
- Familiarity with EHR systems and common clinical documentation practices.
- Strong understanding of data captured in structured EHRs, unstructured EHRs (e.g., clinical notes), and claims datasets, including which types of concepts each source best captures.
- Excellent verbal and written communication skills for cross-functional collaboration.
Preferred
- Advanced training or certification in clinical informatics.
- Minimum of 2–3 years of experience in clinical informatics or a closely related role.
- Demonstrated expertise in semantic mapping of source clinical terms to standard vocabularies (e.g., SNOMED CT, LOINC, RxNorm, ICD-10-CM, CPT, HCPCS).
- Demonstrated analytical and problem-solving skills applied to complex clinical data challenges, such as resolving semantic ambiguity, aligning heterogeneous data sources, or developing scalable mapping solutions.
- Experience defining and reviewing data quality tests on clinical datasets.
- Experience with the OMOP Common Data Model.
- Proficiency with terminology mapping tools.
- Familiarity or experience with AI-based extraction from unstructured clinical notes.
- Strong understanding of the hierarchical structure and relationships in standard clinical terminologies.
- Proficiency in tools for data analysis or transformation (e.g., SQL, Excel, Python, or R).
- Prior involvement in projects involving phenotyping or computable cohort definitions.
- Proven ability to collaborate with technical teams to design and implement repeatable, scalable approaches to knowledge-driven workflows.
- AI-first mindset, with a focus on leveraging automation to develop scalable, repeatable solutions for clinical data normalization, concept identification, and quality assessment.