Prasenjit Singh

Prasenjit Singh leads Milliman’s data engineering, architecture, and data science function for Medicaid and commercial health clients. He helps to shape cloud-native platforms that power multi-state claims, eligibility, and clinical analytics used in high-visibility policy and financial decisions. He directs a multidisciplinary team of engineers, architects, data scientists, and analysts that design governed data pipelines, AI-driven analytics, and decision-support applications for rate setting, program integrity, network adequacy, maternal health, GLP-1 therapy evaluation, and other core Medicaid initiatives.

His recent work includes architecting a unified Medicaid ingestion and orchestration framework across several states; deploying AI-enabled outlier detection and fraud, waste, and abuse risk-scoring with explainable, audit-ready models; and building retrieval-augmented intelligence systems that surface policy, actuarial, and contract insights with traceable citations. These solutions operate under end-to-end governance aligned with HIPAA, HITRUST, GDPR, SOC, and FedRAMP expectations, strengthening data quality, compressing analytics timelines, and giving state agencies faster, more reliable access to decision-ready information.