Jingang Miao
Data Scientist
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolJingang Miao is a data scientist with seven years of industry experience and a PhD in Statistics from Texas A&M, currently applying advanced measurement and privacy techniques at Meta after recent roles at Apple and Waymo. His background spans ads measurement, responsible AI, differential privacy, and reliability, with senior analytics roles across Google, Facebook/Twitter, and enterprise finance at AIG. Trained also in science and technology journalism, he brings uncommon strengths in translating complex statistical research into clear narratives for product and policy stakeholders. He has a multidisciplinary undergraduate foundation in biomedicine, English, and e-business from Peking University, which informs a pragmatic, cross-domain approach to data problems. Colleagues rely on him for rigorous experimental design and privacy-aware solutions that balance business impact with methodological integrity.
7 years of coding experience
11 years of employment as a software developer
BA Biomedicine & English, BA Biomedicine & English at Peking UniversityPhD Statistics, PhD Statistics at Texas A&M University
Chinese
Github Skills (50)
python10
data-science10
data-manipulation10
dataframes10
pandas10
machine-learning10
manipulation10
deep-learning10
tensorflow10
data-structures10
frame10
documentation10
data-analysis10
translation9
deep-neural-networks9
Programming languages (3)
Github contributions (5)
google/empirical_calibration
Feb 2019 - Aug 2022
Contributions:2 reviews, 48 commits, 4 PRs in 3 years 7 months
facebookresearch/mc
Sep 2020 - Nov 2021
I have implemented in both python and R two papers for estimating subgroup means under misclassification, which are useful for data analyses. T. K. MAK, W. K. LI, A new method for estimating subgroup means under misclassification, Biometrika, Volume 75, Issue 1, March 1988, Pages 105–111, https//doi.org/10.1093/biomet/75.1.105 Selén, Jan. “Adjusting for Errors in Classification and Measurement in the Analysis of Partly and Purely Categorical Data.” Journal of the American Statistical Association, vol. 81, no. 393, 1986, pp. 75–81. JSTOR, www.jstor.org/stable/2287969. Accessed 10 Aug. 2020.
Contributions:48 commits, 4 PRs, 18 pushes in 1 year 2 months
jstorpythondata-analysesanalysesjournal
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