Felipe Dias
Principal Machine Learning Engineer at Creditas
São Paulo, Brazil
Summary
👤
Senior🎓
Top SchoolFelipe Dias is a Principal Machine Learning Engineer with 12 years of experience building production-grade ML and data platforms in financial services, currently leading Creditas’s ML, master data, governance and customer data initiatives from Valencia. He combines strong software engineering rigor (Kotlin, Python, AWS) with hands-on MLops practice—designing feature stores, model lifecycle tooling (MLflow) and scalable batch/online deployments. Felipe bridges research and production: his academic path includes MSc and ongoing PhD work and earlier contributions to query optimization and graph search at Inria, informing pragmatic solutions for complex data problems. He mentors staff engineers, runs tech talks and is active in open-source recruiting challenges where he implemented credit analysis models and improved maintainability. Beyond technical leadership he founded Atypicals, Creditas’s neurodivergence inclusion group, reflecting a commitment to inclusive engineering cultures. Colleagues know him for pairing deep systems thinking with a bias for operational simplicity.
12 years of coding experience
6 years of employment as a software developer
Linux Systems Administrator, Hardware and Logic Programming, Linux Systems Administrator, Hardware and Logic Programming at SenacDoctor of Philosophy - PhD, Information Systems, Doctor of Philosophy - PhD, Information Systems at USP - Universidade de São PauloBachelor's Degree, Software Engineering, Bachelor's Degree, Software Engineering at Budapest University of Technology and EconomicsFederal University of Rio Grande do NorteDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Nantes UniversitéDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at École Polytechnique
English, Portuguese, French, Hungarian
Github Skills (15)
machine-learning10
python10
flask-ask9
flask9
fastapi9
scikit-learn8
scikit8
ch8
ember4
javascript3
javas3
ruby3
java3
dockers3
docker3
Programming languages (13)
Github contributions (5)
Creditas/challenge
Jan 2020 - Jun 2020
Team recruiting challenges
Role in this project:
ML Engineer
ML EngineerContributions:10 commits, 9 pushes in 4 months
Contributions summary:Felipe contributed to the project by implementing and integrating a machine learning model for credit analysis. They refactored code, potentially improving the codebase's maintainability. Furthermore, they added a new challenge, likely providing a practical exercise related to machine learning engineering tasks within the context of the repository. This indicates involvement in the end-to-end ML pipeline.
hiringjavascriptrubyrecruitingember
fcas/superset
Jun 2017 - May 2024
Contributions:1 push in 7 years
data-scienceweb-applicationmachine-learningbusiness-intelligencesuperset
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