Clinical Data Manager

Viz.ai

22h•Remote

About The Position

Viz.ai is the leader in building and deploying AI-powered Care Pathways and helping doctors do their work. The Viz Platform is deployed in 2,000 hospitals across the United States and trusted by many of the leading life sciences companies. The platform uniquely combines real-time, multimodal clinical data with deep clinician engagement to detect disease earlier, coordinate care teams, and help ensure patients receive the right treatment faster. Viz.ai was the first company to be awarded CMS reimbursement for AI and is ranked the #1 Healthcare AI Platform by hospitals and health systems in the Black Book Research survey. For more information, visit Viz.ai. Role Overview: This is not a traditional eCRF-only Data Manager role. We are hiring a leader who can build and run research databases, operationalize EHR→EDC automation, and own data quality through data lock - while performing light-to-moderate statistical analysis as needed. This position plays a critical role in driving Viz’s Evidence Generation strategy by managing the data life-cycle for clinical research and quality improvement studies. This role requires expertise in creating sophisticated data management systems, performing statistical analyses, automating data integrations, and ensuring robust data integrity practices.

Requirements

5–7+ years in clinical research/RWE data management with hands-on database build + cleaning/QC ownership.
SQL + Python (or R) for data transformation, QC checks, and reproducible pipelines.
Experience with EHR or EHR-derived datasets and understanding of common structures/coding systems (ICD-10, CPT, LOINC, RxNorm preferred).
Practical familiarity with API-based ingestion and integrating multiple data sources/models.
Experience building/owning DMPs/SOPs/runbooks and maintaining audit-ready documentation.
Familiarity with integrating leading AI techniques into your work product.

Nice To Haves

Experience with EDC platforms (Medidata Rave, REDCap, Castor, Veeva, etc.).
CDISC familiarity (SDTM/ADaM) or strong equivalent standardization experience.
Stats experience in real-world/implementation studies (propensity methods, time-to-event, mixed models) — not required to be a PhD biostatistician

Responsibilities

Configure/maintain EDC forms and eCRF specifications as needed to support ingestion and downstream analysis, focusing on scalability and standardization.
Own end-to-end Data Management for Evidence Generation studies: build, validate, and maintain research databases from ingest → cleaning → QC → data lock.
Develop and run data cleaning workflows (queries, reconciliation, audit trails), and ensure inspection-ready documentation.
Design and operate data model strategy for automated ingestion of EHR-level RWE into the EDC.
Work hands-on with APIs/integration endpoints and unify disparate data models (site/EHR variability, mapping logic, versioning, schema evolution).
Partner with site IT/informatics and the Product team to understand EHR constraints, extract structures, and change management.
Translate EHR data realities into feasible study data capture and monitoring plans.
Create and maintain DMPs, SAPs, SOPs, runbooks, and training materials that operationalize the above processes across care pathways.
Perform statistical analyses on cleaned datasets (descriptive, comparative, time-to-event where appropriate) and support evidence packages and reporting.