Clinical Informaticist

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.