About
Yujun Zhou
Data Science Manager & Tech Lead · Advertising and media group
Employers and internal products are described by category rather than by name. Specifics are on my resume and I am happy to go through them in conversation.
I’m a data scientist with a PhD in Applied Economics. I spent several years on recommendation quality and integrity measurement at a large short-form video platform, and I now lead data science for enterprise AI systems at an advertising and media group. Across both, my job is to define an outcome worth moving, test whether the evidence supports the decision, and build whatever is missing to check it.
The through line is measurement under pressure. Engagement moves for reasons that have nothing to do with a better product. An assistant that reads well can still fail the task it was hired for. Most of my work is deciding which comparison is credible enough to act on, and saying plainly when it isn’t.
I build the systems I need to test my own ideas — evaluation pipelines, data contracts, and small products with real users — because a claim I cannot reproduce is not evidence I can defend.

Experience
Where the work happened
Advertising and media group
Data Science Manager & Tech Lead
Enterprise AI evaluation: layered task metrics, human rubrics, regression gates, and the quality, cost, and latency tradeoffs behind a release decision.
Short-form video platform
Data Scientist
Two measurement problems in one product, which is why they are listed apart — they fail in different ways and need different evaluation populations.
- Recommendation quality
- Defining consumption-side outcomes for a short-form video feed, reading experiments against them, and pairing a creator-side goal with a viewer-experience guardrail.
- Integrity measurement
- Evaluating rare, high-cost failures, where a sample drawn for average performance does not have the resolution the decision requires.
Education
Training
University of Illinois Urbana-Champaign
PhD, Applied Economics
Causal identification, forecasting, and the gap between a model that fits and a model a decision-maker can use. Published in Applied Economic Perspectives and Policy, World Development, and JAFIO.
Toolkit
What I reach for
Measurement & inference
- Experiment design
- Causal inference
- Metric definition
- Sampling & power
- Offline–online gap analysis
AI evaluation
- Human rubrics & annotator agreement
- Failure taxonomies
- LLM-as-judge validation
- Calibration & abstention
- Regression gates
Building
- Python
- SQL
- FastAPI
- React / TypeScript
- Postgres & pgvector
Research
Published work
- 2022
Machine learning for food security: Principles for transparency and usability
Applied Economic Perspectives and Policy
- 2020
Effects of stockholding policy on maize prices: Evidence from Zambia
Journal of Agricultural & Food Industrial Organization
- 2019
A data-driven approach improves food insecurity crisis prediction
World Development
Reference
From a former manager
Consistent excellence in analytics work, with strong influence skills, operational rigor, and a high bar for data-driven decision making.