Jose H - Advisor at Repsol

Jose H

Advisor at Repsol

Madrid, Community of Madrid, Spain

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Summary

👤

Senior

🎓

Top School

Jose H is a PhD-level AI executive and advisor based in Madrid with 11 years of experience translating deep research into industrial impact across energy, telecom and enterprise sectors. Currently advising Repsol Technology Lab after leading advanced ML and reinforcement learning initiatives there, he has a track record of shipping production-ready AI solutions and guiding strategic adoption of state-of-the-art methods. His background blends academic rigor—summa cum laude PhD in Computer Science and AI—with hands-on roles as Head of Data Science and CTO-level engineering leadership. He contributes to open-source ML tooling, improving core deep reinforcement learning libraries with practical enhancements like training progress, logging, and data-structure refactors. Comfortable operating at the intersection of research, product and management, he also holds advanced studies in psychology and executive management, signaling an uncommon mix of technical depth and human-centered leadership.

11 years of coding experience

12 years of employment as a software developer

Lic. Computación, Computer Science, Lic. Computación, Computer Science at Universidad del Zulia

Global Management Programme, Global Management Programme at IESE Business School - University of Navarra

Advanced Studies Diploma (DEA), Psychology, Advanced Studies Diploma (DEA), Psychology at Universidad Nacional de Educación a Distancia - U.N.E.D.

Advanced Studies Diploma (DEA), Computer Science, TOP, Advanced Studies Diploma (DEA), Computer Science, TOP at Universidad Politécnica de Madrid

Spanish, English

Github Skills (6)

deep-reinforcement-learning10

pytorch10

deeplearning-ai10

deep-learning10

python10

tqdm9

Programming languages (7)

TypeScriptC++ShellScalaJavaScriptJupyter NotebookPython

Github contributions (5)

takuseno/d3rlpy

Nov 2020 - Jul 2021

An offline deep reinforcement learning library

Role in this project:

Back-end Developer

Contributions:23 reviews, 30 commits, 10 PRs in 8 months

Contributions summary:Jose's commits focused on improving the core functionalities of the offline deep reinforcement learning library. They added features to the base learner, including saving loss history and referencing the active logger. Further contributions involved refactoring the code, such as redefining data structures to use `defaultdict`, and adding a progress bar using `tqdm` to enhance the training loop. They also introduced a shuffling parameter for the training process.

pytorchdeep-learningreinforcement-learningdeep-reinforcement-learningoffline-rl

jamartinh/Orange3-Spark

Nov 2015 - Dec 2016

Contributions:10 releases, 75 commits, 2 PRs in 1 year 1 month

orangepythondata-sciencespark-mlmachine-learning

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