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# Data Scientist

- **Company:** Ascension
- **Location:** United States (Remote available)
- **Salary:** $96,209.0 - $134,110.0
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Intelligence, BigQuery, Health Informatics, Clinical Data Repository, Relational Databases, Github, Revision Control Systems, Python (Programming Language), Machine Learning, NumPy, Standard Sql, Feature Engineering, Large Language Models, Prompt Engineering, Model Validation, Electronic Medical Records, Generative AI, Pandas, Scikit Learn, Information Technology, HuggingFace, Health Level Seven International, Machine Learning Operations
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=38851435cbb38c6e

## About the Role

* High School diploma equivalency with 2 years of cumulative experience OR Associate's
degree/Bachelor's degree OR 4 years of applicable cumulative job specific experience required.

What additional requirements you'll need:

 * Education: A Bachelor's, Master's, or PhD in Data Science, Computer Science, Health Informatics, Statistics, or a related STEM field. Healthcare Domain Interest: Familiarity with or strong eagerness to learn healthcare data standards and clinical coding structures (e.g., ICD-10, CPT codes).
 * Core Technical Skills: Solid foundational proficiency in Python (pandas, NumPy, scikit-learn), working knowledge of SQL (BigQuery/relational databases), and familiarity with version control tools like Git/GitHub. AI & NLP Exposure: Exposure to Large Language Model (LLM) concepts, prompt engineering, or modern NLP/GenAI frameworks (e.g., Hugging Face, LangChain, or Google Vertex AI) through coursework, research, internships, or projects.
 * Communication & Curiosity: Strong analytical problem-solving skills with the ability to communicate technical findings clearly to both technical peers and clinical stakeholders.

## Description

* Build & Refine: Assist in building, testing, and refining predictive machine learning models and text-based Generative AI tools (utilizing Vertex AI and modern NLP frameworks) under the guidance of senior team members to address real-world clinical challenges.
 * Process & Analyze: Extract, clean, and preprocess structured healthcare data (EHR, billing, and claims), performing feature engineering to address noise, missingness, and complex clinical data structures.
 * Evaluate & Test: Conduct model evaluation, benchmarking, and error analysis, assessing performance metrics alongside clinical safety, fairness, and potential algorithmic bias.
 * Collaborate & Learn: Work alongside senior data scientists and clinical mentors to translate broad clinical questions into structured analytics problems and actionable technical tasks.
 * Document & Handoff: Write clean, well-documented code and maintain clear project documentation (in GitHub) to support model deployment and integration with MLOps pipelines.


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