Role Overview

Viz.ai’s AI-powered care coordination platform helps care teams get patients to the right treatment faster. Our Evidence Generation team shows, with rigorous real-world data, how that translates into better outcomes for patients, providers, health systems, and sponsors.

You will build the data foundation for that evidence. You’ll design automated pipelines that turn raw, messy EHR data into clean, audit-ready research datasets, and you’ll own the quality of those datasets from first ingest through data lock. If you like solving hard data-mapping problems and care that the answer is provably correct, this is a rare chance to do it where the results reach real patients.

Key Responsibilities:

Health Data Engineering & Automated Pipelines

• Design and maintain version-controlled Python/SQL pipelines that transform raw EHR extracts (FHIR bundles, HL7v2 messages, flat files) into structured, EDC-ready datasets, handling field mapping, unit harmonization, deduplication, and complex derivation logic.

• Reconcile disparate data models, adapting to site-specific EHR variability, mapping constraints, and schema changes.

• Apply LLM and NLP techniques to extract structured clinical variables from unstructured sources (clinical notes, pathology and radiology reports, discharge summaries), and validate their accuracy.

Clinical Data Management (CDM) & Quality Control

• Drive end-to-end data management for Evidence Generation studies, spanning data ingestion, cleaning, quality control (QC), and data lock.

• Implement programmatic QC workflows to perform automated anomaly detection, field mapping validation, query generation, and audit trail checks, significantly reducing manual review burden.

• Build and maintain data dictionaries, EDC forms, and eCRF specifications that support standardized ingestion and downstream analytics.

• Ensure inspection-ready documentation, maintaining strict data provenance and compliance throughout the dataset lifecycle.

Site Strategy, Governance, & Enablement

• Partner with site IT/informatics and internal teams to navigate EHR system constraints and translate complex site data realities into feasible study data capture plans.

• Author and maintain foundational clinical governance documents, including Data Management Plans (DMPs), SOPs, runbooks, and transformation/mapping specifications.

• Guide internal teams on process optimization and best practices for interacting with real-world health datasets across varying care pathways.

• Collaborate closely with Israel-based engineering and AI teams to align technical operations and pipeline development.

You Will Thrive in This Role If:

• You enjoy working with raw, complex EHR data and take satisfaction in engineering clean, structured, study-ready datasets out of messy health system extracts.

• You are strong in Python and SQL, passionate about building automated, reproducible ETL pipelines, and eager to leverage modern LLM/NLP tools to extract structured data from unstructured clinical text.

• You value clinical rigor and compliance, remaining meticulous about data integrity, audit trails, and maintainable documentation standards (such as DMPs and SOPs).

• You excel at technical problem-solving, adept at navigating complex field-mapping challenges while balancing immediate study timelines with long-term pipeline scalability.

• You are a collaborative communicator, skilled at translating technical EHR/data pipeline realities into clear, actionable insights for diverse stakeholders across product, clinical, and health systems.

Qualifications:

Required

• 5+ years in clinical research, real-world evidence (RWE), or health informatics.

• Proven ownership of research database builds, including cleaning, QC, and data lock.

• Strong SQL and Python skills for building automated ETL pipelines, transformations, and programmatic QC checks.

• Hands-on experience transforming raw EHR extracts (FHIR, HL7v2, or flat-file exports) into structured, EDC-ready datasets.

• Familiarity with clinical terminologies and coding systems (e.g., ICD-10, CPT, LOINC, RxNorm) and standard health data structures.

• Experience handling data model variations across different health systems.

• Experience writing and maintaining governance collateral, including Data Management Plans (DMPs), SOPs, runbooks, and mapping specifications.

• Working knowledge of version control and reproducible-pipeline practices (e.g., Git) to keep transformation code auditable.

• Familiarity with LLM/NLP tools for extracting structured variables from unstructured clinical text (e.g., progress notes, radiology reports, pathology records).

• Familiarity with regulated-data requirements [GCP, 21 CFR Part 11, HIPAA].

Preferred

• Hands-on experience with major EDC platforms (e.g., REDCap, Castor, Veeva).

• Familiarity with standardized health and clinical trial data models (e.g., OMOP CDM, CDISC SDTM/ADaM, PCORnet).

• Understanding of FHIR resource structure and how EHR data is exported.

What Success Looks Like:

• First 30 days: You understand our EHR data sources, active studies, and current data management process, and you’ve met your key site and internal stakeholders.

• By 60 days: Standardized database templates are in use, and data dictionaries and eCRFs are built (or updated) for active studies. An agreed SDV/QA approach is documented.

• By 90 days: A first raw-EHR-to-EDC pipeline is built and validated for at least one care pathway, with clear audit trails and reproducible outputs.

Why should you join us?

• If you are looking to make an impact, we are mission-driven and are making a difference in peoples’ lives every day.

#LI: GH1

#LI: remote