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Epic Clarity Machine Learning Jobs (NOW HIRING)

Marencor

AI/ML Data Scientist

New York, NY · On-site

... machine learning * Experience with Snowflake SQL , Python ML ecosystem, healthcare data Preferred Qualifications: * Exposure to Epic Clarity data or unstructured clinical notes * Hands-on with ...

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Epic Clarity Machine Learning information

What is an Epic Clarity Machine Learning professional?

An Epic Clarity Machine Learning professional specializes in leveraging Epic's Clarity database to develop and deploy machine learning models that improve healthcare operations and patient outcomes. They work with large datasets extracted from Epic's electronic health records (EHR) system, using statistical and machine learning techniques to identify patterns, predict trends, and support decision-making. Their role often involves data extraction, cleaning, model development, and collaboration with clinical and IT teams to implement actionable insights within healthcare organizations.

What are the key skills and qualifications needed to thrive as an Epic Clarity Machine Learning Specialist, and why are they important?

To thrive as an Epic Clarity Machine Learning Specialist, you need expertise in healthcare data analytics, SQL, and machine learning techniques, usually backed by experience with Epic Clarity databases and a background in computer science or a related field. Familiarity with Epic Clarity reporting tools, Python or R for modeling, and Epic certifications (such as Clarity Data Model or Cogito) are commonly required. Strong problem-solving ability, communication skills, and attention to detail set standout specialists apart, enabling them to translate complex data into actionable insights. These skills are vital for optimizing healthcare outcomes, ensuring data accuracy, and supporting evidence-based decision-making in clinical environments.

How does an Epic Clarity Machine Learning professional typically collaborate with clinical and IT teams on healthcare analytics projects?

Epic Clarity Machine Learning professionals regularly work alongside both clinical teams and IT specialists to design, develop, and implement predictive analytics solutions within the Epic ecosystem. They help translate clinical requirements into actionable data models, ensuring that the resulting insights are clinically relevant and technically feasible. Collaboration often involves regular meetings to refine project goals, troubleshoot data issues, and validate model outputs with subject matter experts, fostering a cross-functional environment that bridges healthcare and data science.
Infographic showing various Epic Clarity Machine Learning job openings in the United States as of May 2026, with employment types broken down into 1% Locum Tenens, 75% Full Time, and 24% Part Time. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

AI/ML Data Scientist

Marencor

New York, NY • On-site

Contractor

Posted 5 days ago

Job description

 AI/ML Data Scientist

 Location: New York, NY

Role Summary:

Develop and evaluate machine learning and GenAI models to drive clinical, operational, or financial insights from healthcare data.

 

Responsibilities:

  • Train supervised and unsupervised models using Python (XGBoost, LightGBM, sklearn, PyTorch)
  • Conduct data profiling, feature engineering, model evaluation using stratified validation
  • Implement model explainability (SHAP, LIME) and bias audits
  • Monitor model drift, fairness, and clinical relevance over time
  • Fine-tune or prompt LLMs for use cases such as summarization or information extraction
  • Build and evaluate multi-agent workflows using GenAI and frameworks like LangChain
  • Design prompt libraries and benchmark hallucination rates, sensitivity to input phrasing
  • Track experiments using MLflow, Weights & Biases

 

Required Qualifications:

  • 3–5 years in applied data science or machine learning
  • Experience with Snowflake SQL, Python ML ecosystem, healthcare data

 

Preferred Qualifications:

  • Exposure to Epic Clarity data or unstructured clinical notes
  • Hands-on with transformer models, LLM APIs, and embedding-based retrieval