Data Scientist

Posted Friday, August 28, 2026 at 5:00 AM

Department: Technology

Location: Remote

FLSA Status: Exempt

Travel: Less than 10%

Company Summary

Accuity partners with hospitals and health systems through a technology-enabled, physician-led model that improves clinical documentation integrity, coding accuracy, reimbursement optimization, and quality outcomes.

The Data Scientist develops, evaluates, and monitors the predictive and generative AI systems that drive automation across Accuity’s clinical documentation and revenue cycle workflows. Reporting to the Vice President, Data Science and AI, this role combines classical machine learning with applied work on large language model and agent-based systems, and is accountable for establishing whether those systems are measurably good enough to be trusted inside a clinical workflow.

Evaluation is the spine of the role. The Data Scientist builds and maintains the ground truth datasets, evaluation frameworks, and metrics that determine production readiness, designs samples so results are comparable and defensible, and analyzes performance by clinical and payer segment rather than in aggregate. This includes independently reproducing and pressure-testing results reported by external AI development partners rather than accepting them as delivered.

This role owns methodology, evaluation, and model development. Production deployment, serving infrastructure, and the engineering of AI systems into live workflows are owned by AI Engineering, and the Data Scientist partners closely with that function to move validated work into production.

• Develop, train, and improve predictive and machine learning models supporting chart triage, prioritization, documentation integrity, and revenue cycle outcomes.
• Maintain and improve models already running in production, including retraining cadence, feature review, threshold tuning, and recalibration as data and workflow change.
• Identify and correct sampling and selection bias in training data, including bias introduced when a model’s own decisions determine which records are subsequently observed.
• Contribute to applied work on large language model and agent-based systems, including prompt and workflow design, tool integration, and systematic failure mode analysis.
• Build feedback loops so findings from clinical audit and production review flow back into model, threshold, and system decisions rather than stopping at a report.
• Prototype and test new approaches, and state plainly and early when an approach does not work.

• Build and maintain ground truth datasets, including sourcing, annotation coordination with clinical subject matter experts, quality assessment, and remediation of known data defects.
• Design evaluation samples deliberately, understanding when a stratified or weighted sample is appropriate, when a near-production distribution is required, and when results across runs are not comparable.
• Define, compute, and document the metrics that determine production readiness, and ensure another person can reproduce them from source data.
• Analyze performance by clinically meaningful segment, including diagnosis group, service line, payer, and case complexity, rather than reporting aggregate accuracy alone.
• Analyze error in both directions, distinguishing false positives from false negatives and quantifying the clinical and financial consequence of each.
• Maintain evaluation tooling and harnesses so experiments are repeatable and every result is traceable to a specific dataset, configuration, and version.

• Monitor deployed models and AI systems for accuracy, calibration, drift,…