Role Summary:
Develop and evaluate machine learning and GenAl models to drive clinical, operational, or financial insights from healthcare data.
Responsibilities:
Train supervised and unsupervised models using Python (XGBoost, LightGBM, sklearn, Pylorch)
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 GenAl and frameworks like
LangChain
Design prompt libraries and benchmark hallucination rates, sensitivity to input phrasing
Track experiments using MiFlow, Weights & Biases
Required Qualifications:
From an overall 9 to 10 years atleast 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
Mandatory Skills
Aiml, Machine Learning
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