Ming Li
Data Scientist
Stony Stratford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolMing Li is a seasoned Data Scientist with a decade of experience applying machine learning and applied research across fintech, ecommerce, utilities, healthcare, and enterprise settings. He combines deep scientific Python expertise (pandas, PyTorch/TensorFlow, probabilistic libraries) with engineering skills for production deployment (pyspark, FastAPI, Docker, AWS/GCP), and has a track record of shipping models into real-world systems. Ming contributes to high-profile open-source ML libraries such as pandas and scikit-learn, focusing on time-series robustness, bug fixes and clearer documentation—work that improves reliability for many downstream users. His background spans senior and lead roles at companies like Zilch, Just Eat Takeaway.com and Anglo American, reflecting both technical depth and cross-industry adaptability. Outside work he pursues arts and travel, bringing a curious, interdisciplinary perspective to problem-solving.
10 years of coding experience
11 years of employment as a software developer
Master's degree, Master's degree at Cranfield UniversityDeep Learning, Deep Learning at UdacityData Scientist, Data Scientist at Dataquest.ioMachine Learning, Machine Learning at Stanford University
Stackoverflow
139reputation
79kreached
6answers
0questions
Github Skills (19)
debug10
python10
data-science10
testing10
scikit10
pandas10
machine-learning10
time-series10
scikit-learn10
data-analysis10
documentation8
numpy7
neural-network6
bayesian6
google-app-engine6
Programming languages (3)
Github contributions (5)
pandas-dev/pandas
Jan 2018 - Dec 2018
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
Data Scientist
Data ScientistContributions:25 commits, 62 PRs, 212 comments in 11 months
Contributions summary:Ming primarily contributes to bug fixes and enhancements within the pandas library, focusing on time series functionalities and data manipulation. Their work includes addressing issues related to `pct_change` and other time series methods, as well as fixing a recursion error when handling out-of-bound datetimes during replacement operations. These contributions highlight a focus on maintaining and improving the accuracy and robustness of pandas for data analysis tasks. Furthermore, they are also involved with updates to documentation.
pythondatalabeled-datamanipulationdataframes
scikit-learn/scikit-learn
Apr 2018 - Sep 2019
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:7 commits, 13 PRs, 52 comments in 1 year 5 months
Contributions summary:Ming contributed to the scikit-learn repository by addressing multiple issues related to the machine learning library. Their work included improving error messages in the scoring functions, fixing a bug in the IncrementalPCA, and improving the documentation of R-squared. Additionally, the user adjusted tolerances in a test related to linear discriminant analysis. These commits suggest a focus on bug fixes, documentation, and test adjustments within the context of machine learning algorithms.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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