Maxime Dumonal
Engagement Director Data Scientist
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolMaxime Dumonal is an Engagement Director and Data Scientist based in Zurich with 9 years of data-focused experience and a two-decade background bridging quantitative trading and machine learning. He leads client engagements at Unit8, translating advanced time-series and forecasting methods into production-ready solutions while maintaining hands-on contributions to open-source tooling. His notable work on the popular darts library improved multivariate forecasting (N-BEATS) and reliability through testing and bug fixes, highlighting a pragmatic blend of research and software engineering. Earlier roles span fintech and prop trading, where he built algorithmic strategies and founded a digital wealth-management startup that was subsequently acquired. He holds engineering and statistics degrees from CentraleSupélec and Paris I, plus a Stanford certificate in mining massive data sets, reflecting strong theoretical grounding. Maxime is comfortable moving between boardroom strategy and low-level model implementation, often surfacing subtle data-transformation issues before they reach production.
9 years of coding experience
11 years of employment as a software developer
Master of Science (MSc), ENGINEERING, Master of Science (MSc), ENGINEERING at CentraleSupelecMaster of Science (MSc), Mathematical Statistics and Probability, Master of Science (MSc), Mathematical Statistics and Probability at University of Paris I: Panthéon-SorbonneGraduate Certificate, Computer Sciences, Mining Massive Data Sets, Graduate Certificate, Computer Sciences, Mining Massive Data Sets at Stanford University
japanese (n3), English, German, French
Github Skills (13)
unit-testing10
forecasting10
machine-learning10
forecast10
time-series10
anomaly-detection10
python10
data-science10
scikit-learn9
scikit9
deeplearning-ai9
deep-learning9
statsmodels8
Programming languages (1)
Github contributions (5)
unit8co/darts
May 2021 - Jan 2023
A python library for user-friendly forecasting and anomaly detection on time series.
Role in this project:
Data Scientist
Data ScientistContributions:44 reviews, 168 commits, 16 PRs in 1 year 8 months
Contributions summary:Maxime contributed significantly to the `darts` library by implementing and testing multivariate time series forecasting models, specifically the NBEATS model. Their work included modifying the model's architecture and adding unit tests to ensure functionality and accuracy, which included the use of covariates. They also addressed and fixed bugs related to data transformation and the implementation of Granger causality tests. Their contributions centered around improving the library's capabilities in time series analysis and machine learning.
forecastingpython-libraryanomalypythontime-series-analysis
dumjax/Stanford
Jan 2018 - Aug 2018
Contributions:24 pushes, 1 branch in 6 months
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