Advisory & Thought Partnership

Let’s Talk

I occasionally advise teams working on decision systems, evaluation frameworks, and analytics strategy. I enjoy conversations about turning ambiguous, high-stakes problems into decision frameworks that teams can actually use.

My background: research-grade evaluation rigor (PhD in multi-omics analysis) + product judgment (drug discovery ML). I design frameworks from first principles, not templates.

Topics I Think About

Designing Decision Frameworks

• How to structure decision contexts for ambiguous problems
• Building north star + guardrails that actually guide prioritization
• Stakeholder alignment when definitions of “good” conflict

Evaluation Systems for ML & Data Products

• Offline validation + online monitoring architectures
• Preventing “metric theater” in model evaluation
• What counts as evidence when ground truth is delayed or absent

Analytics Strategy & Capability Building

• Moving from ad-hoc analyses to repeatable decision systems
• Aligning analytics with business OKRs
• Building analytics functions that accelerate decision velocity

Technical Architecture for Data Systems

• Tradeoff analysis for build vs. buy decisions
• Data pipeline optimization for cost, latency, reliability
• Monitoring and observability for production systems

How I Approach These Conversations

I focus on understanding your decision context first—what’s uncertain, what’s constrained, what counts as “good enough.” Then I work through options analysis and tradeoff documentation to make decisions defensible to stakeholders.

I don’t deliver generic best practices. Every framework is designed from first principles for your specific constraints.

Examples of My Thinking

Want to see how I approach decision systems? Read my case studies:

• Preventing Metric Theater in Drug Discovery ML
• Reducing Pipeline Latency to Accelerate Decision Velocity

Let’s Connect

Interested in a conversation? Book a time below.