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💡 Javier Orraca-Deatcu  
├─🚀 Professional Overview  
│ ├─💼 Career  
│ │ ├─ Centene / Lead Machine Learning Engineer  
│ │ ├─ Bloomreach / Sr Manager, Data Ops & Analytics  
│ │ ├─ Centene / Data Scientist III, Strategic Insights  
│ │ ├─ KPMG / Sr Manager, Economics & Valuation  
│ │ ├─ PG&E / Supervisor, Capital Recovery & Analysis  
│ │ └─ EY / Manager, Valuation, Modeling, & Economics  
│ └─📚 Education  
│   ├─ UC Irvine / MSc Business Analytics  
│   └─ Georgia Tech / BSc Management & Finance  
├─📦 Projects  
│ ├─ Personal Blog - Data science blog  
│ ├─ Scatter Podcast - Analytics & data science in business  
│ └─ SoCal R Users Group - Events coordinator & volunteer   
└─💡 Media Appearances & Public Events  
   ├─ Forbes - Scatter Podcast mention on Forbes  
   ├─ Data Points Conference - R for Analytics Engineering   
   ├─ UC Irvine Newsroom - Scatter Podcast in UC Irvine news   
   └─ LatinX Initiative Conference - Panelist at UC Irvine   

📫 Contact:   
├─ LinkedIn: https://www.linkedin.com/in/orraca   
└─ Twitter: @JavierOrraca

Javier Orraca-Deatcu's Projects

arrow icon arrow

Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

epa-air-quality-analysis icon epa-air-quality-analysis

Exploring the usage of public Google BigQuery data sets, exporting queries from Google Cloud Platform to Colab, and developing Python script to test usage of Spark and see how computationally efficient it can be on a Colab Jupyter notebook.

llama_in_r icon llama_in_r

Walkthrough of Posit AI blog's LLaMA in R with Keras and TensorFlow (w/ modified code for LLaMA-2-7b)

quarto-blog icon quarto-blog

Personal data science website rebuilt as a Quarto blog

quarto-club icon quarto-club

A super gallery of quarto websites to help inspire and connect.

robyn icon robyn

Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) code from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich dataset.

socalr.org icon socalr.org

Source code for the Southern California R Users Website

unsupervised-ml icon unsupervised-ml

A short exercise using R to perform unsupervised machine learning (clustering) on a sample data set.

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