David Arpin
Seattle, Washington, United States
Summary
🤩
Rockstar
Top expert inAWS Cloud Computing and Machine Learning Development
David Arpin is a Senior Manager of Data Science in Seattle with 12 years of experience building and scaling ML and analytics teams across e-commerce and cloud platforms. He has led large production efforts—from architecting real-time Python serving APIs and automated retraining pipelines at Indeed to shaping and launching Amazon SageMaker and its popular awslabs example notebooks. His work spans computer vision, NLP, forecasting, recommender systems and custom reinforcement learning solutions, demonstrating both product-facing leadership and hands-on ML engineering. He’s grown teams, influenced senior leadership on ad monetization strategy, and presented at AWS re:Invent, illustrating a rare mix of technical depth, operational rigor, and persuasive business impact. An early career foundation in discrete choice, econometrics and multi-terabyte data modeling informs his data-driven approach to pricing, forecasting, and experimentation.
12 years of coding experience
Github Skills (6)
xgboost10
jupyter-notebook10
machine-learning10
amazon-sagemaker10
data-science10
r9
Programming languages (3)
Github contributions (5)
aws/amazon-sagemaker-examples
Oct 2017 - Apr 2020
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:314 commits, 183 PRs, 157 pushes in 2 years 6 months
Contributions summary:David contributed to the Amazon SageMaker examples repository by adding a new end-to-end example for multiclass classification using the XGBoost algorithm. The user implemented the installation of an R kernel, integrated the XGBoost model with the direct marketing dataset and included a direct marketing example notebook. Their work involved data preprocessing, model training, and hosting setup within the Amazon SageMaker environment using a Jupyter Notebook. This demonstrates their ability to build, train, and deploy machine learning models using Amazon SageMaker.
amazon-sagemakerjupyter-notebookmachine-learning-modelssagemakeraws
djarpin/tabularasa
Aug 2022 - Jan 2023
Tabular PyTorch Neural Network with support for monotonic features and aleatoric and epistemic uncertainty estimates using a scikit-learn API
Contributions:8 PRs, 8 pushes, 8 branches in 5 months
tabularpythonmonotonicpreprocessingscikit
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