Marios Michailidis
Director Of Field Technology EMEA at H2O.ai
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolMarios Michailidis is a data science leader and Director of Field Technology EMEA at H2O.ai with over a decade of experience applying advanced analytics across credit, marketing and personalization domains. A Kaggle Grandmaster who once ranked 1st among ~500,000 members, he combines competitive ML expertise with production-facing engineering—authoring Java tools like KazAnova and the StackNet meta-modelling framework used for scalable model stacking and cross-validation. He has led analytics programs spanning acquisition, retention, uplift, fraud detection, recommenders and portfolio optimisation for UK financial and retail clients, and has a track record of turning research into shipping solutions. Comfortable across Java, Python, R, SQL and big-data workflows, he mentors teams and embeds robust data-prep and IO capabilities into product-grade frameworks. His academic background includes PhD work in financial computing at UCL, reflecting a strong bridge between research rigor and pragmatic deployment.
10 years of coding experience
11 years of employment as a software developer
Msc Corporate and Security Management, Msc Corporate and Security Management at University of Southampton6.93 (Merit) Ba in Accounting and Finance, 6.93 (Merit) Ba in Accounting and Finance at University of MacedoniaUniversity College London
English, Greek, python ;), java ;), c++ ;), r ;)
Stackoverflow
11reputation
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Github Skills (13)
data-structures10
javas10
data-structure10
java10
data-processing10
cross-validation10
exception-handling9
jenkins6
cron6
flatten6
timetable6
google-sheets6
filter6
Programming languages (5)
Github contributions (5)
kaz-Anova/StackNet
Mar 2017 - Sep 2018
StackNet is a computational, scalable and analytical Meta modelling framework
Role in this project:
Back-end Developer
Back-end DeveloperContributions:122 commits, 6 PRs, 123 pushes in 1 year 6 months
Contributions summary:Marios primarily contributed to the `crossvalidation.splits` package, developing a `RandomSplit` class for splitting datasets, including target variables, weights, and various data types (int, string, double). They also created exception handling classes related to data validation. These commits indicate a focus on data preparation and manipulation functionalities, core to a meta-modelling framework. The user also worked on input/output functionalities, including reading data.
modelling-frameworkscalablepythonmetaanalytical
kaz-Anova/ensemble_amazon
Apr 2016 - Apr 2016
Contributions:20 commits, 19 pushes, 1 branch in 1 day
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