Daniel Formoso
Advisor And Investor at No Brand Holdings
Sydney, New South Wales, Australia
Summary
🤩
Rockstar🎓
Top SchoolDaniel Formoso is an AI product and operations leader with 11+ years of experience blending large-scale cloud engineering, enterprise MLOps, and frontier research at Google Brain/DeepMind. He has architected and scaled HPC genomics environments to 60,000+ cores, led operational integration during major research mergers, and now designs agentic workflows and autonomous harnesses informed by hands-on applied ML. Equally comfortable with strategic OKR redesign and the nitty-gritty of dataset sourcing and TPU allocation, he bridges research rigor and productization to deliver AI-native ecosystems at scale. An active practitioner—authoring classification notebooks and contributing data-science work in public repos—he pairs a Master’s in Data Science and research training in bioinformatics with investor/advisor roles in Australian startups.
11 years of coding experience
11 years of employment as a software developer
Bachelor's Degree Electronics Engineering, Bachelor's Degree Electronics Engineering at Universidad Simón BolívarThe University of SydneyUNSW Sydney
English, Spanish
Github Skills (21)
python10
jupyter10
data-science10
classification10
scikit10
pandas10
machine-learning10
scikit-learn10
evaluation9
feature-engineering9
data-cleaning9
eval9
k-nearest-neighbours8
logistic-regression8
random-forest8
Programming languages (5)
Github contributions (5)
dformoso/sklearn-classification
Aug 2017 - Jul 2018
Data Science Notebook on a Classification Task, using sklearn and Tensorflow.
Role in this project:
Data Scientist
Data ScientistContributions:37 commits, 37 pushes, 1 branch in 11 months
Contributions summary:Daniel's commits primarily involve data science tasks within a classification project. Their contributions focus on data cleaning, feature engineering, and building various machine learning models for the census income dataset. They demonstrated the application of preprocessing techniques, model selection, and evaluation to achieve classification goals. The commits show iterative model development and analysis, including the use of logistic regression, KNN, and other models, as well as random search hyperparameter tuning.
classificationdata-sciencesklearntensorflowdata
etsangsplk/splunk-jupyter
Jul 2017 - Sep 2017
Contributions:24 commits in 1 month
pythondata-sciencedataframejupyter-notebooknotebook
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