Manuel Campo

Sant Quirze del Vallès, Catalonia, Spain

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Summary

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Rockstar

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Top School

Manuel Campo is a mathematician-turned-software engineer with 10 years of experience building data-driven systems across finance and IT, currently focused on data engineering and ML-enabled back ends. He co-founded two consultancies and has led squads at Abacum, combining hands-on coding with product and team leadership under tight deadlines. Manuel contributes to notable open-source projects in synthetic data and time-series anomaly detection, improving maintainability and adding core modeling features for libraries like SDV and Copulas. His strengths lie in mathematical abstraction, pragmatic engineering, and a persistent curiosity about why systems fail and how to make them better. Comfortable moving between architecture, testing, and dependency management, he brings both analytical rigor and operational discipline to production ML workflows. Based in Catalonia, he pairs startup grit with a track record of sensible refactors and thoughtful tooling contributions that quietly improve project health.

10 years of coding experience

5 years of employment as a software developer

Grado en Matematicas, Matematicas, Grado en Matematicas, Matematicas at Universitat Autònoma de Barcelona

English, French, Catalan, Spanish

Github Skills (19)

data-generation10

anomaly-detection10

python10

scikit10

machine-learning10

time-series10

refactor10

scikit-learn10

refactoring10

documentation10

data-science9

generative-adversarial-network9

scipy9

lib9

testing7

Programming languages (5)

C++HTMLJupyter NotebookPythonKotlin

Github contributions (5)

sdv-dev/Copulas

May 2018 - Aug 2019

A library to model multivariate data using copulas.

Role in this project:

Back-end Developer

Contributions:257 commits, 44 PRs, 116 pushes in 1 year 3 months

Contributions summary:Manuel's commits primarily focused on refactoring and enhancing the codebase of a library for modeling multivariate data using copulas. They updated author information, fixed typos, and moved code out of the root directory. These changes suggest a focus on improving code organization, maintainability, and correctness.

pythontabular-datagenerative-modelmachine-learningdataset-generation

sdv-dev/SDV

Jul 2018 - Aug 2019

Synthetic data generation for tabular data

Role in this project:

Back-end Developer & Data Scientist

Contributions:1 release, 192 commits, 29 PRs in 1 year 1 month

Contributions summary:Manuel made substantial changes to the codebase, including modifying documentation files and updating the project's author details. The commits suggest a focus on project setup and documentation improvements, with the user updating dependencies and the documentation configuration. Furthermore, the user has been working on internal architecture of the code by integrating new functionalities like creating an option for model database and adding the possibility to model the amount of child rows.

relational-datasetssynthetictime-seriessynthetic-datamulti-table

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