David聽Masip
Frontend Software Engineer at Cognizant Netcentric
Barcelona, Catalonia, Spain
Summary
馃懁
Senior馃帗
Top SchoolDavid Masip is a frontend software engineer based in Barcelona with nine years of professional experience and a strong academic grounding in web and multiplatform development from La Salle. He has focused recent work on React and advanced ES6+, intentionally upskilling to be productive from day one and standing out as a specialist in modern front-end stacks. His background spans startups and consultancies鈥攂uilding UI/UX-driven websites, landing pages, and AI-enabled drone software鈥攄emonstrating versatility across product, deployment, and testing. As a contributor to the scikit-learn-contrib category_encoders project, he brings data-science sensibilities to engineering, improving core encoding logic and test coverage. Recognized early in his studies (student of the year) and seasoned by an Erasmus in Germany, he pairs strong communication and self-sufficiency with practical, cross-disciplinary problem solving. Currently focused on frontend roles at Cognizant Netcentric, he blends a mathematician/physicist mindset with hands-on implementation to deliver polished, testable interfaces.
9 years of coding experience
3 years of employment as a software developer
Desarrollo de aplicaciones web Programaci贸n inform谩tica, Desarrollo de aplicaciones web Programaci贸n inform谩tica at La Salle de Gr脿ciaBasic SEO course Marketing, Basic SEO course Marketing at Noble Work FoundationElectricity and Automation, Electricity and Automation at Salesians TerrassaES6+, ES6+ at Scrimba
Catalan, Spanish, English
Stackoverflow
2,391reputation
211kreached
23answers
106questions
machine-learning
top-5%
image
top-5%
neural-network
top-5%
computer-vision
top-5%
Github Skills (16)
scikit-learn10
pandas10
machine-learning10
python10
data-science10
scikit10
computer-vision9
image9
neural-network9
regression6
decision-tree6
google-bigquery6
logistic-regression6
jinja26
pygsheets6
Programming languages (7)
Github contributions (5)
scikit-learn-contrib/category_encoders
Oct 2021 - Oct 2021
A library of sklearn compatible categorical variable encoders
Role in this project:
Data Scientist
Data ScientistContributions:6 commits in 3 days
Contributions summary:David primarily contributes to the `category_encoders` library, focusing on the SummaryEncoder and QuantileEncoder. Their work includes refactoring existing code, adding default arguments, fixing test failures related to encoders, and parametrizing the SummaryEncoder. The user's commits show they are actively working on the core encoding functionalities within the library, with a focus on improving existing implementations and extending the test coverage.
sklearnsklearn-compatibleencodersvariablecategorical
cmougan/CERN_kaggle
Oct 2020 - Nov 2020
Contributions:56 commits, 21 pushes, 1 branch in 25 days
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