Philip Stevens

Data Scientist and Machine Learning Engineer

Summary

Spent the last year travelling with my wife and now looking to work remotely for a while.

I have over 5 years experience as a data scientist and machine learning engineer with a proven history building end-to-end machine learning systems at scale in the context of digital advertising. I also have a MSc in Computer Science and experience with academic research in machine learning.

I have several years of experience with Python and it's various machine learning and data science libraries. I have a strong understanding of and experience using a wide variety of machine learning algorithms, concepts, and techniques. I have a strong interest in artificial intelligence in general, and deep learning and reinforcement learning in particular. I have successfully applied techniques in these domains in my previous role and a number of projects.

Favorite Python Packages:

tensorflow, keras, pandas, scikit-learn, luigi, seaborn

Experience

• Struq/Quantcas
  
  • Struq acquired by Quantcast
  • 5 years: Oct 2013 - Sep 2018
  • Designed, implemented and operated large scale machine learning systems in the context of digital advertising
  • Built data pipelines to prepare training data and input features then train, tune, evaluate, compare, select and export models
  • Performed offline evaluation and selection of different models, modelling improvements and other prototyped solutions
  • Designed and implemented many input features to predictive models from user and contextual data including complex derived features
  • Ran large scale A/B experiments to evaluate model improvements in a live production environment.
  • Investigated modelling issues, identified root causes, and designed solutions.
  • Interpreted and communicated results of experiments and analyses within technical teams, to commercial teams and to clients directly
• Tumra
  • Data science internship where I helped develop recommendation engines and conducted A/B tests for landing pages
• University of Auckland
  • Worked as research assistant conducting research in sketch element recognition on tablets
  • Resulted in publication

Skills

Amazon Web Services (AWS), Artificial Intelligence, Big Data, Data Science, Docker, Flask, Git, Hadoop, Keras, Linux, Machine Learning, Natural Language Processing, NumPy, Pandas, SQL, SciPy, Scrapy, Spark, TensorFlow