Senior Clinical Informaticist

About Mosai Mosai™ is the intelligent care coordination platform that brings together the fragmented pieces of healthcare into a clear, connected picture. Like a mosaic, our platform unites data, people, and processes so providers can make better decisions, coordinate care in real time, and deliver improved outcomes. With Mosai, home-based care organizations can thrive in value-based care while giving every patient the right care, in the right place, at the right time. Learn more at​  https://www.mosai.com/   Position Summary Mosai is looking for a Senior Clinical Informaticist to be the clinical conscience of its AI and help drive AI-assisted health care at home. Working with data scientists and ML engineers, this person will own the clinical ground truth used to train and evaluate models, including what gets labeled, how it gets labeled, and whether outputs are safe and correct for nurses, therapists, and hospice teams to use. As the first hire in the clinical informatics function, this hands-on senior leader will create annotation guidelines, label and adjudicate data, build gold-standard datasets, and lead clinical evaluation of models and LLM-based features. The role brings knowledge of how care is delivered and documented at home into the data and logic used by Mosai's AI, while helping build the clinical informatics team. Job Duties: Own clinical annotation: design labeling schemas and versioned guidelines for clinical notes, OASIS items, diagnoses, medications, wounds, functional status, and other home health and hospice concepts. Label and adjudicate data directly; engage appropriate in-house clinical resources, resolve annotator disagreements, make final clinical calls on edge cases, and build and maintain core gold-standard datasets for data science models. Work with MLOps and data science teams to report and monitor model performance and respond to drift in key accuracy metrics. Measure inter-annotator agreement using standard metrics, such as Cohen's or Fleiss' kappa; run calibration sessions, audit samples, and improve annotation guidelines and workflows. Evaluate model predictions and LLM-generated text for accuracy, completeness, hallucination, bias, and clinical safety. Design evaluation rubrics and help define clinical criteria for release of traditional ML and LLM-based models. Translate home health and hospice workflows, including start of care, recertification, resumption of care, discharge, hospice admission, and IDG, into structured data, rules, and features. Map clinical concepts to ICD-10-CM, SNOMED CT, LOINC, RxNorm, OASIS, and hospice HOPE data elements; support EHR integration, including HomeCare HomeBase, using HL7 and FHIR. Define clinical data quality checks, document provenance and dataset versions, and apply HIPAA, minimum-necessary, and de-identification practices to labeling work. Support development of AI capabilities by demonstrating how skilled clinicians perform the same…