Clinical Science Informaticist

About the role

Oracle Health Data Intelligence (HDI) is at the forefront of transforming healthcare through

innovative data and AI solutions. We’re seeking a highly skilled individual contributor to join

our team in the USA. This role focuses on leveraging clinical data, machine learning (ML), and

AI technologies to drive healthcare innovation. You’ll contribute to AI\-driven projects by

applying informatics techniques, statistical modeling, and annotation guidelines to improve

healthcare delivery, patient outcomes, and operational efficiency. At HDI, we are committed to

using cutting\-edge technologies such as natural language processing (NLP) and predictive

analytics to revolutionize patient care and optimize healthcare systems.

Career Level \- IC4

Clinical experience in roles such as a registered nurse, pharmacist, clinical laboratory

technician, respiratory therapist, or other clinical roles. Alternatively, relevant

experience may be considered in place of formal clinical certifications.

Proven experience in clinical informatics, including familiarity with clinical EHR systems

and data, as well as collaboration with healthcare professionals, IT specialists, and

business users to analyze workflows and identify opportunities for improvement.

Strong background in working with clinical data to support evidence\-based decision

making, quality measurement, care coordination, and outcomes\-based improvement

programs.

Hands\-on experience in supporting data annotation, creating and maintaining

annotation guidelines, machine learning (ML), natural language processing (NLP), and

AI\-driven projects within the healthcare domain.

Creation of evaluation frameworks for the performance AI models.

Understanding of the AI model life cycle

Experience at the intersection of statistical methods, machine learning techniques, and

generative AI and medical standards and ontologies.

Expertise in data preprocessing, feature engineering, and model development for

AI/ML applications, with a focus on clinical data integration.

Experience in defining data requirements, ensuring data readiness, and validating

annotated data for AI/ML solutions.

Proficient in working with clinical terminologies such as SNOMED CT, ICD, LOINC, and

CPT, and utilizing them in data science models and algorithms.

Familiarity with healthcare data standards such as FHIR Resources, QDM Categories,

and experience modeling clinical and administrative healthcare data for AI\-driven

solutions.

Proven ability to implement quality control processes for ensuring the integrity,

reliability, and clinical relevance of data used in AI/ML applications.

Experience in working with disparate healthcare data types, including EHR, billing, lab,

eligibility, and claims data, to drive insights and improve healthcare outcomes.

Life sciences, clinical trials, and regulatory experience is a plus.

Create and curate clinical value sets composed of industry\-standard terminologies

such as SNOMED CT, ICD, and CPT, ensuring alignment with data science models,

organizational data models, and algorithms.

Develop and document clear annotation guidelines to ensure they are understood by

annotation teams and data scientists.

Define data requirements and ensure integration within AI/ML\-driven applications,

with a focus on data quality and model readiness.

Implement quality control processes to validate the integrity and reliability of

annotated data, ensuring suitability for AI/ML solutions.

Lead annotation review cycles and provide feedback to ensure labeling quality, while

performing regular evaluations of model predictions to identify edge cases and

improve performance.

Defining AI red teaming and guardrails in collaboration with applied scientists.

Conduct error analysis of AI model outputs.

Recognized as a subject matter expert within the team and provide mentorship to less

experienced team members.

Drive internal platform changes including data models, terminology ontologies, and

the platform rules engine to ensure data compatibility with AI/ML models.

Oversee the collection, cleaning, and pre\-processing of data, ensuring datasets are

ready for analysis and model training.

Define requirements for establishing the effectiveness of AI/ML models by designing

ground truth algorithms and performance metrics for outcome validation.

Collaborate with cross\-functional teams, including data scientists, annotators,

engineers, and project managers, to enhance data quality and success across AI\-driven

projects.

Contribute to model evaluation and performance monitoring, ensuring data\-driven

insights meet clinical objectives.

Continue to drive and implement AI driven automated processes, both within the

annotation framework and other job expectations

Skills

Strong critical thinking and problem\-solving skills, particularly for designing algorithms

and models that meet clinical and business objectives within real\-world healthcare data

constraints.

Experience working with disparate healthcare data including EHR, billing, lab,

eligibility, or claims data.

Expertise in clinical and administrative healthcare data modeling using industry

standards such as FHIR Resources, QDM Categories, and other healthcare data

formats.

Advanced knowledge of data science techniques including statistical analysis, machine

learning, and natural language processing (NLP).

Proficiency in programming languages such as Python, R, or SQL for healthcare data

analysis and model development.

Experience with AI/ML frameworks such as TensorFlow, PyTorch, or scikit\-learn.

Familiarity with industry\-standard terminologies such as SNOMED CT, ICD, LOINC, and

CPT, and their application in data science models.

Knowledge of care management best practices and services and their integration with

AI/ML models.

Strong ability to interpret complex clinical and business requirements, transforming

them into actionable data guidelines and annotations.

Excellent communication and collaboration skills, working effectively with data

scientists, annotators, engineers, and project managers to drive successful AI projects.

Knowledge of model evaluation metrics and ability to conduct model validation,

ensuring that AI\-driven solutions deliver accurate and actionable clinical insights.