Panos Mavrokonstantis
Principal Data Scientist
Singapore
Summary
🤩
Rockstar🎓
Top SchoolPanos Mavrokonstantis is a Principal Data Scientist based in Singapore with seven years of industry experience advancing machine learning and econometrics at scale, currently leading data science efforts at Grab. He blends a PhD-level background in economics from LSE with hands-on ML engineering, having built multi-task learning models for financial prediction and productionized analytics for treasury and operations. At Grab he progressed from Senior to Lead to Principal, demonstrating impact in both technical delivery and team leadership across product domains. His academic work applied machine learning to causal inference on population-scale labor data, an uncommon mix that informs robust, policy-aware modeling in commercial settings. Outside of industry roles he has taught data science courses and translated rigorous research into actionable business insights, frequently communicating complex methods to non-technical stakeholders.
7 years of coding experience
11 years of employment as a software developer
Economics, Economics at Harvard University Extension SchoolMaster of Science (MSc) Economics for Development, Master of Science (MSc) Economics for Development at University of OxfordBachelor of Science (BSc) Philosophy Politics and Economics, Bachelor of Science (BSc) Philosophy Politics and Economics at University of WarwickEducation Abroad Program Economics, Education Abroad Program Economics at UC Santa BarbaraLondon School of Economics and Political Science
Greek, English, Russian
Github Skills (19)
project-structure9
cookiecutter9
standardized9
python8
science8
data-sharing8
ai8
machine-learning8
data-science8
gmm7
linear-models7
random-effects6
panel-data6
regression5
prediction-model5
Programming languages (3)
Github contributions (5)
mavpanos/bunching
Apr 2019 - Aug 2022
Contributions:182 commits, 23 PRs, 198 pushes in 3 years 4 months
mavpanos/cookiecutter-data-science
May 2023 - May 2023
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Contributions:10 PRs, 5 pushes, 9 branches in 1 day
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