Marcelo Lacerda
Research Data Scientist, Machine Learning
Pernambuco, Brazil
Summary
👤
Senior🎓
Top SchoolMarcelo Lacerda is a research-oriented machine learning scientist with a PhD in Computer Science and over a decade of experience bridging academic research and applied AI. He specializes in swarm intelligence, evolutionary computation and reinforcement learning, applying these methods to bioinformatics and computational drug discovery while leading ML initiatives at Iambic Therapeutics. Marcelo has a strong track record in production-ready data engineering and NLP for regulated domains, from building ETL pipelines for the high-profile Serenata de Amor civic-AI project to legal document classification and state-level AI tooling. He has taught graduate data science and undergraduate computing courses, reflecting a talent for translating complex research into practical solutions. Based in Pernambuco, Brazil, he combines rigorous academic credentials with industry delivery across startups, government and enterprise teams.
10 years of coding experience
9 years of employment as a software developer
Master of Engineering - MEng, Computer Engineering, A, Master of Engineering - MEng, Computer Engineering, A at Universidade de PernambucoDoctor of Philosophy - PhD, Computer Science, 3.83 (0-4), Doctor of Philosophy - PhD, Computer Science, 3.83 (0-4) at Universidade Federal de Pernambuco
Portuguese, English, Spanish, German
Github Skills (9)
pandas10
open-data10
data-pipelines10
data-cleaning10
data-pipeline10
python10
data-engineering10
file-handling9
data-transformation9
Programming languages (5)
Github contributions (5)
okfn-brasil/serenata-de-amor
Oct 2017 - Dec 2017
🕵 Artificial Intelligence for social control of public administration | **This repository does not receive frequent updates. Check out the README**
Role in this project:
Data Engineer
Data EngineerContributions:13 commits, 1 PR, 20 comments in 1 month
Contributions summary:Marcelo primarily focused on data extraction, transformation, and loading (ETL) processes for campaign donation data. They developed scripts to download data from external sources, parse and clean datasets, and prepare the data for analysis. The contributions include data retrieval from various URLs, data cleaning, and data formatting, indicating a strong focus on building data pipelines.
civic-techdata-scienceadministrationpoliticsartificial
lacerdamarcelo/cec17_python
May 2019 - May 2019
Contributions:6 commits, 4 pushes, 1 branch in 1 day
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