About lemma
Applied science for real world decisions.
Lemma is a consulting practice focused on statistics, data science, decision intelligence, and practical AI. We help organizations frame complex questions, build the right analytical systems, and turn evidence into decisions that can be acted on.
But first, why Lemma? In mathematics, a lemma is a proven result used as a stepping stone of sorts toward a larger conclusion. It helps establish the reasoning needed to solve a more complex problem, but is not the complete solution itself.
This idea reflects how we work and see ourselves in your workflow: we break difficult questions into tractable parts, establish what the evidence supports, and use each result to move toward a clearer decision.
→ But we are not the complete solution; your business is.
We simply provide the needed tools to help strengthen and grow what you’ve already built.
WHY LEMMA EXISTS
Many organizations have more data, tools, and technical capability than ever, but still struggle to connect those resources to clear decisions and reliable workflows.
Lemma exists to close that gap by…
combining rigorous data science methods with practical implementation, so models, dashboards, forecasts, and AI systems work in harmony and are useful in the environments where they operate.
HOW WE WORK
We begin with the problem, not the tech stack.
We spend sufficient time defining the decision, understanding the data and constraints, selecting the appropriate method, and building something that is interpretable, maintainable, and usable in practice.
Focused engagements
Start with a clear operational, analytical, or strategic question.
Appropriate methods
Employ the level of complexity the problem requires.
Practical delivery
Build outputs that can be understood, adopted, and maintained.
about our founder
Philip Waggoner, Ph.D.
Data scientist and architect at many organizations including Yale, Stanford, University of Chicago, Mosaic (AI startup), YouGov, Columbia University, and others. Specializes in translating complexity into actionable applied science, dashboarding, AI/ML infrastructure, data science pipelines, and scalable analytics systems.