Applied AI & Data Science · Ph.D. Economics

Hi, I'm Preston Monk.

I'm an economist who builds AI systems for businesses. I want to understand the whole thing: where the data comes from, what it means, how a model uses it, and whether the answer is reliable enough for someone to act on. That takes me across data engineering, data science, and AI engineering. I don't really see three separate disciplines. I see one system.

Preston Monk
  • 01 Data engineering
  • 02 Data science
  • 03 AI engineering

Philosophy.

What a business knows is rarely in one place. Some of it sits in a transactional database. Some is in manufacturer documentation nobody has opened in years. Some is in a spreadsheet one person maintains. Some exists only as a rule an experienced employee applies without thinking about it. All of it is evidence, and none of it was organized for a machine to reason over.

This is where I have seen AI systems fail. By an AI system I mean an application built on a foundation model — Claude or GPT — where the model is a component we call, not something we train. We supply the question and all of the context it answers from. Everything that determines whether the answer is right happens before the model is called. Hand it a partial, inconsistent picture and it will still answer, fluently, with no way to check it. The fix is not a better prompt. It is representing what a business knows in a form its people can query.

Building that representation is most of the work, and it doesn't sit inside a single discipline. Whether an answer can be trusted depends on how the data was assembled, what the modeling can support, and how the system retrieves and checks its evidence. In most organizations those are three separate jobs, often three separate teams: data engineering, data science, and AI engineering. But the decisions constrain each other. Dividing them is how systems end up fluent and unreliable.

So I work across all three. My training is in economics — a discipline about decisions under uncertainty: what causes what, what a measurement is really capturing, how much weight a result can bear. A language model producing the answer makes those questions harder to ask and easier to skip.

My goal is to understand this entire stack. Not every corner of software — this one: how a business turns what it knows into systems that can reason over it, and whether the answers are good enough to act on.

Case study

What I'm building.

Toolkit

What I work with.

Applied AI & retrieval
Large language models, retrieval-augmented generation, hybrid retrieval architectures, semantic search, embeddings, knowledge graphs, graph traversal, SQL retrieval, document retrieval, prompt and context engineering, tool calling, AI agents, LLM evaluation
Data science & machine learning
Statistical modeling, predictive modeling, recommendation systems, forecasting, causal inference, feature engineering, model evaluation, A/B testing, anomaly detection, customer analytics, decision support systems
Languages
Python, SQL, TypeScript, R, Git
Data engineering & platforms
PostgreSQL, vector databases, PySpark, Azure Fabric, AWS, Docker, ETL pipelines
Writing

Thinking out loud.

Notes on working across disciplines that used to require separate careers, and on the gap between a system that answers and a system you can trust. First pieces landing soon.

  • 01 Why AI makes the polymath viable again Draft
  • 02 Why retrieval usually beats a bigger model Draft
  • 03 Evaluating AI when there is no ground truth Draft
About

How I got here.

I completed my Ph.D. in Economics at Florida State in 2024, based at the DeVoe L. Moore Center. My dissertation linked millions of property transactions to demographic, economic, environmental, and public administrative records, building spatial models of how properties, neighborhoods, schools, crime, and infrastructure interact. I used difference-in-differences, nearest-neighbor matching, and spatial econometrics to identify how neighborhood shocks move property values and push households to relocate.

Both halves of that were demanding. Assembling records that were never designed to be joined took years, and every construction decision had to hold up under a committee. The modeling is what made it mean anything: an estimate is only as credible as the identification strategy behind it, and building that strategy is the work. The data and the model are not separable — a careless join produces a confident, wrong result.

That is the training I brought into AI engineering. Economists are taught to worry about identification — whether the thing you measured is really the thing you meant to measure — and to treat a claim nobody can verify as no claim at all. Those instincts matter more, not less, once a language model is producing fluent answers at speed. It is why I build systems that carry their evidence: where an answer came from, what supports it, and how much weight it can hold.

Preston is based in Miami, where he lives the American Dream with his wife Malaya and son Rainer.

Ph.D. Economics — 2024
Florida State University
M.S. Economics — 2019
Florida State University
B.S.B.A. Economics — 2018
University of South Alabama