Hang Zhang - Principal Data & Applied Scientist Manager

Hang Zhang

Principal Data & Applied Scientist Manager

Redmond, Washington, United States

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Summary

👤

Senior

🎓

Top School

Huanan Zhang is a senior machine learning engineering leader with 11 years of experience building large-scale relevance and personalization systems, currently leading search relevance efforts at Atlassian after a multi-year tenure at Microsoft. At Microsoft he architected production pipelines that linked billions of URLs to hundreds of millions of business IDs, launched Topic Search for Bing/Bing Maps, and advanced personalization using LLMs and RAG across cross-platform signals. He combines hands-on data science—contributing to Azure ML sample and TDSP utilities—with team leadership, shipping categorization-as-a-service and end-to-end local search stacks at scale. With a PhD in computer science and a track record of turning research-grade models into robust production services, he’s adept at bridging product, UX, and engineering to improve real-world search outcomes.

11 years of coding experience

13 years of employment as a software developer

Master Automation, Master Automation at Tsinghua University

Master of Science Statistics, Master of Science Statistics at Rutgers University

Chinese

Github Skills (15)

data-visualizations10

pandas10

jupyter-notebook10

machine-learning10

data-visualization10

data-visualisation10

python10

data-science10

data-analysis10

modeling9

feature-engineering9

trainings9

scikit7

scikit-learn7

Programming languages (5)

RGoHTMLJupyter NotebookPython

Github contributions (5)

Azure/Azure-TDSP-Utilities

Sep 2016 - Aug 2017

Utilities and scripts developed as part of Microsoft's Team Data Science Process for productive data science

Role in this project:

Data Scientist

Contributions:36 commits, 5 PRs, 37 pushes in 11 months

Contributions summary:Hang contributed to the development of interactive data exploration and reporting tools. Their work involved modifying and improving existing Jupyter notebooks (IDEAR) using Python, specifically focusing on functionalities such as data summarization, descriptive statistics, variable interaction analysis, and data visualization through t-SNE. They also fixed bugs and updated R script references within the project.

pythonsciencedata-scienceteam-datamachine-learning

Azure-Samples/Azure-MachineLearning-DataScience

Jan 2015 - Aug 2017

Role in this project:

Data Scientist

Contributions:169 commits, 13 PRs, 154 pushes in 2 years 6 months

Contributions summary:Hang primarily contributed to the data science process within the repository. Their work focused on creating, modifying, and integrating iPython Notebooks that involved data processing, feature engineering, and model training. The user’s changes involved tasks like renaming notebooks, modifying raw text within notebooks, and merging branches, suggesting a focus on the data science workflow and model exploration.

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