Decision Intelligence
Frame problems, define metrics, and design systems that support real actions.
Senior data scientist - applied AI, ML, and decision intelligence
I work across problem framing, data strategy, modeling, evaluation, and operating constraints to turn ambiguous business questions into decision-grade systems.
Capabilities
Frame problems, define metrics, and design systems that support real actions.
Use structured data, text, time-based signals, and embeddings for prediction and retrieval.
Build confidence through calibration, error analysis, experiments, and measurement design.
Anticipate monitoring, interpretability, failure modes, and stakeholder adoption.
Application Areas
Risk, operations, and regulated analytics
Decision systems for provider risk, operational escalation, claims signals, utilization, and stakeholder-facing healthcare analytics.
Signals, alerts, and market-aware workflows
Lightweight data products that turn time-sensitive financial events into usable alerts, research context, and decision support.
Prioritization systems for messy business workflows
Models and analytics workflows that help teams decide what to review, escalate, investigate, or act on next.
Grounded assistants and practical LLM workflows
Retrieval-augmented systems and LLM prototypes designed around grounding, evaluation, compute constraints, and practical usefulness.
Selected Work
Healthcare Decision Support
Survival and ML-based risk modeling to predict provider escalations before they become operationally expensive.
GenAI and Retrieval Systems
Local LLM and retrieval pipeline for answering Medicare questions with grounded source material.
Finance and Macro Intelligence
Automated calendar extraction and alerting pipeline for market-moving macro or earnings events.
Low-friction next step
Email is the best first step. No scheduling flow, no form, no friction.