Paulo Meira
Research Scientist
São Paulo, Brazil
Summary
👤
Senior🎓
Top SchoolPaulo Meira is a research scientist and IT infrastructure leader with over 20 years of experience spanning program management, IT operations, consulting and people management across mainframe, midrange, network, cloud and digital platforms. He led large multidisciplinary teams—supporting roughly 600 staff—bringing strong end-to-end process, governance and financial planning expertise from a long tenure at IBM. Paulo combines technical depth with academic research ties (EE researcher at Unicamp, PhD) and practical software contributions, notably maintaining and modernizing the popular NILMTK non-intrusive load monitoring toolkit for Python 3 compatibility. Fluent in English and Spanish and MBA-trained in IT management, he excels at aligning technical services with business controls, sales support and auditing. Known as a results-oriented talent developer, he blends ITIL/Agile/DevOps practices and design thinking to drive reliable, auditable solutions. An understated strength is his cross-domain fluency—from low-level infrastructure to data-science code maintenance—making him effective at bridging research, engineering and operations.
10 years of coding experience
21 years of employment as a software developer
MBA, IT Management, MBA, IT Management at FGV - Fundação Getulio VargasPost-Graduated, Administration & Marketing, Post-Graduated, Administration & Marketing at Universidade São Francisco
Github Skills (10)
pandas10
algorithms10
python10
data-analysis9
machine-learning9
testing8
numpy8
notebook7
ipython7
jupyter-notebook7
Programming languages (13)
Github contributions (5)
nilmtk/nilmtk
Feb 2018 - Mar 2021
Non-Intrusive Load Monitoring Toolkit (nilmtk)
Role in this project:
Back-end Developer / Data Scientist
Back-end Developer / Data ScientistContributions:3 releases, 3 reviews, 130 commits in 3 years 1 month
Contributions summary:Paulo primarily focused on updating and maintaining the NILMTK toolkit. They made substantial contributions to improve Python 3 compatibility, update deprecated Pandas and HMMLearn calls, and address various compatibility issues. Their work involved modifications to existing code within the disaggregation algorithms, tests, and data processing, demonstrating a focus on ensuring the toolkit's functionality and adapting to evolving library versions.
forecastingpythonnilm-algorithmsnilmelectrical-engineering
BaluJr/energytk
Feb 2018 - Sep 2018
Non-Intrusive Load Monitoring Toolkit (nilmtk)
Contributions:71 commits in 7 months
monitoring
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