We're looking for passionate professionals who can bridge traditional machine learning and cutting-edge GenAI to solve real-world healthcare challenges.
Check all associated application documentation thoroughly before clicking on the apply button at the bottom of this description.
What we're looking for:
Strong foundation in ML concepts – supervised/unsupervised learning, uplift modeling, time-to-event, calibration, bias/variance, drift & monitoring
Hands-on experience with GenAI / LLMs and classical ML approaches
Expertise in:
Document understanding (prior auth, EOBs)
Summarization, classification, entity extraction
Retrieval-Augmented Generation (RAG), prompt design & evaluation
Ability to design robust evaluation frameworks:
Offline metrics (Precision/Recall, AUROC, MAE) xsgimln
Business KPIs & human-in-the-loop QA
LLM evaluations (groundedness, hallucination, toxicity, PHI leakage)
Working knowledge of tools like Python, SQL, notebooks, ML lifecycle tools (MLflow or similar)