Clinical AI Data Specialist

Role Overview

• 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
• 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 output labels against clinical documentation to identify false positives, false negatives, and specificity errors; clean and correct label datasets and categorize error patterns for the data science team
• Apply coding knowledge to evaluate whether model-generated code assignments are clinically and regulatorily supportable, and escalate systematic quality issues that may indicate model behavior problems
• Translate ICD-10-CM and coding guideline requirements into explicit, testable instructions — LLM prompt language and computable coding rules — using AI-assisted tools testing revisions against curated ground-truth datasets and iterating on observed failures
• Document the clinical rationale and precision/recall impact of each prompt or rule change for senior review

Requirements

• Domain expertise with a minimum 5 years of coding and/or CDI experience with demonstrated proficiency in ICD-10-CM code assignment from clinical documentation
• Active credential in at least one of: CCS, CPC, CRC, CDIP, CCDS, or equivalent AHIMA/AAPC certification
• Ability to apply clinical coding standards consistently and independently to produce high-quality, reproducible labels across large document sets, catching subtle distinctions that affect code assignment
• Ability to articulate the clinical rationale behind a labeling decision in writing for QA and audit, and to express coding requirements as explicit, unambiguous instructions — the discipline behind a well-constructed coding query
• Works independently within established guidelines without case-by-case direction on routine annotation, and escalates systematic issues — repeated error patterns, guideline gaps, documentation quality trends — rather than resolving them in isolation

Benefits

• Comprehensive health, dental, and vision insurance
• Paid time off (PTO) plan, offering X days per year, plus holidays
• Retirement savings plan
• Flexible work arrangements
• Opportunities for career growth and development
• Employee wellness programs