Luis Arenas
Tech Lead Data Scientist ML Eng. (Product Innovation)
Berlin, Germany
Summary
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Senior🎓
Top SchoolLuis Arenas is a Tech Lead Data Scientist and ML Engineer with nine years of experience building end-to-end AI and Big Data products, currently driving product innovation at Deutsche Telekom in Berlin. He leads small international teams to design ML architectures, orchestration, and experimentation pipelines while aligning technical choices with stakeholder-driven product roadmaps. His work blends practical network-domain expertise—developed earlier in optical and wireless roles—with modern self-supervised learning and anomaly detection to improve network optimization and reliability. Luis is hands-on in pre-training strategy and A/B testing, mentors junior data scientists, and has a sustained curiosity for cutting-edge AI research that informs production-grade solutions. An engineer by training (M.Sc. ICE, plus telecom specializations), he brings a rare combination of telecom systems depth and contemporary ML practice to difficult operational problems.
9 years of coding experience
2 years of employment as a software developer
Technischen Universität DarmstadtElectronic Engineer, Electronics, Telecommunications, Automation, Control, Industrial Instrumentation and Television., Electronic Engineer, Electronics, Telecommunications, Automation, Control, Industrial Instrumentation and Television. at Universidad Distrital Francisco José de Caldas
Spanish, German, English
Github Skills (10)
supervised-learning6
image-processing5
machine-learning4
pytorch4
natural-language-processing4
datasets4
toolbox4
deep-learning3
fairness-ml3
ai2
Programming languages (2)
Github contributions (5)
felipevillaarenas/vicreg
Jul 2022 - Oct 2022
Contributions:69 commits, 64 pushes, 1 branch in 2 months
felipevillaarenas/Neural-Machine-Translation-LSTMs-Attention
Apr 2021 - Jun 2023
In Machine Translation, our goal is to convert a sentence from the source language (e.g. Spanish) to the target language (e.g. English). In this assignment, we will implement a sequence-to-sequence (Seq2Seq) network with attention, to build a Neural Machine Translation (NMT) system.
Contributions:4 pushes in 2 years 1 month
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