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Hi there I am Paul 👋

I 💟 Data Science, ML, and MLOps.

I built

  • recommendation systems in ecommerce
  • digital advertising optimization systems
  • personalization algorithms
  • item similarity indices
  • etl & elt jobs

I write

Tools & Technologies

AWS Google Cloud Python R Scala Keras NumPy Pandas PyTorch scikit-learn TensorFlow Streamlit MLflow GE Prefect Apache Airflow Apache Spark BQ Postgres MongoDB Trino GitLab CI PyCharm Atom Vim Jupyter Jenkins Docker

Courses and Certifications

Research

Before I started working in industry as a Data Scientist, I was a research fellow at the Max Planck Institute for empirical Aesthetics. During that time, I published more than 1️⃣4️⃣ Manuscripts, 7️⃣ of them in leading peer-reviewed journals of the field.

📖 Publications 📖

Paul Elvers's Projects

clv icon clv

Calculating Customer Lifetime Value (CLV) with Recency, Frequency, and Monetary (RFM)

decision-forests icon decision-forests

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

example-scripts icon example-scripts

The official example scripts for the Numerai Data Science Tournament

numerapi icon numerapi

Python API and command line interface for the numer.ai machine learning competition

tensorflow-recommendation-wals icon tensorflow-recommendation-wals

An end-to-end solution for website article recommendations based on Google Analytics data. The model is based on matrix-factorization using the WALS algorithm, in TensorFlow, trained on Cloud ML Engine. Recommendations are served using App Engine Flex with Cloud Endpoints. Orchestration is performed using Airflow, running on Cloud Composer or GKE.

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