Ariel Jiang
Data Scientist
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAriel Jiang is a data scientist with 11 years of experience applying Bayesian and machine learning methods to marketing analytics, growth, and product problems at companies including Uber, DoorDash, and Pluto TV. She has built production-grade forecasting and allocation models, run complex experimentation and causal inference designs, and translated results into actionable budget and product decisions. Her work blends strong statistical foundations from a Masters in Policy Analysis with hands-on engineering—contributing to open-source tooling like Uber’s Orbit by improving EDA visualizations and hourly data pipelines. Comfortable across SQL, Python, R, and AWS, she has a track record of turning noisy, large-scale data into robust models and clear stories for nontechnical stakeholders. Notably, she pairs marketing mix and Bayesian time-series expertise with practical deployment experience, bridging research and production. Based in Los Angeles, she focuses on measurable impact: optimizing spend, improving retention, and scaling analytics systems.
11 years of coding experience
8 years of employment as a software developer
Masters, Policy Analysis and Program Evaluation, Masters, Policy Analysis and Program Evaluation at University of MinnesotaData Scientist Nano Degree, Data Science, Data Scientist Nano Degree, Data Science at UdacityBachelor's degree, Economics and Sociology, Bachelor's degree, Economics and Sociology at Peking University
Chinese, English
Github Skills (11)
data-visualizations10
pandas10
jupyter-notebook10
data-visualization10
data-visualisation10
seaborn10
python10
matplotlib10
forecast9
time-series9
forecasting9
Programming languages (1)
Github contributions (5)
uber/orbit
Apr 2021 - Jun 2022
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:2 reviews, 11 commits, 10 PRs in 1 year 2 months
Contributions summary:Ariel primarily contributed to the Orbit project by expanding the color palette, updating EDA plotting functions, and improving the exploratory data analysis (EDA) example notebooks. Their work included updating the orbit style and testing scripts, as well as integrating new hourly data loading capabilities. They also updated residual diagnostic plots, and example notebook and fixed lint problem. The user's contributions touched on both visualization and data analysis aspects of the project.
forecastingpythonregression-modelstimeseries-forecastingforecast
Ariel77/Hackerrank_10_days_of_statistics
Nov 2017 - Nov 2017
Contributions:4 pushes, 1 branch in 19 days
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