Clinical AI Data Scientist

• Location: Remote
• Department: Clinical and Population Health Analytics
• Schedule: Full-time | Day shift

#ADSI #internalops

• 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.

Education:

• 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.

Additional Preferences:

• Technical & GenAI Stack: Degree in a relevant STEM field (A Bachelor’s, Master’s, or PhD in Data Science, Computer Science, Health Informatics, Statistics, or a related STEM field), backed by strong skills in Python (pandas, NumPy, scikit-learn), SQL (BigQuery/relational databases), Git, and practical exposure to LLMs, prompt engineering, or GenAI frameworks (Hugging Face, LangChain, Vertex AI).
• Healthcare Domain & Stakeholder Communication: Curiosity and familiarity with healthcare data standards and clinical coding (e.g., ICD-10, CPT), with proven ability to analyze complex data and translate technical findings to clinical stakeholders.