Datavant is the data collaboration platform trusted for healthcare. Guided by our mission to make the world’s health data secure, accessible and actionable, we provide critical data solutions for organizations across the healthcare ecosystem - including providers, health plans, researchers, and life sciences companies. From fulfilling a single patient’s request for their medical records to powering the AI revolution in healthcare, Datavanters are building the future of how data is connected and used to improve health.
By joining Datavant today, you’re stepping onto a driven and highly collaborative team that is passionate about creating transformative change in healthcare.
What We’re Looking For:
The Data Science / Clinical AI function is seeking a Clinical AI Data Specialist to ensure the clinical accuracy of the training data, model output labels, and clinical logic — prompts and coding rules — that shape how our AI-powered risk adjustment products behave. This is a clinical coding domain-expert role first: it requires active coding credentials and the ability to independently read, interpret, and annotate clinical medical record documentation, and that expertise translates directly into measurable model performance. Errors introduced at this layer propagate into training and produce systematic clinical inaccuracies at production scale, so the quality of your judgment is the product. The technical work — annotation at scale, prompt and rule iteration, and label-quality analysis — is carried out using AI-assisted development tools; we will train the right clinical coding expert on the tooling, and a software engineering background is not required.
What You Will Do:
- Annotate medical records for AI training data
- Validate annotated data to ensure quality
- Refine the clinical logic behind AI outputs
- Provide clinical coding & HIM subject-matter expertise to data science
What a Typical Day Looks Like
In this role, you can expect to:
- Read and interpret clinical documentation — physician notes, assessment and plan sections, problem lists, medication records — to identify codeable diagnoses, conditions, and other clinical entities (document boundaries, type, author, section), applying ICD-10-CM and risk adjustment coding standards and mapping to clinical ontologies (ICD-10-CM/PCS, CPT, RxNorm) when required by project scope
- Distinguish conditions that meet documentation standards for coding from those that do not, exercising clinical judgment independently, and flag ambiguous or edge-case documentation with written rationale}
- Review AI model ou