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.

Yujun Zhou, portrait

Experience

Where the work happened

Read the cases →
  • 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

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.

Former manager, recommendation relevance analyticsAnonymized here at the source. Named reference available on request.