Chloe Mawer

Coming from a background in geophysics and hydrology, Chloe is well-versed in leveraging data to make predictions and provide valuable insights. Holding a PhD in Environmental Engineering, her experience in both academic research and engineering makes her capable of tackling novel problems and creating practical, effective solutions.

Recent Posts

Image Processing in Python

In this post, we go over the steps for creating a proof of concept for the image processing piece of our Caltrain work.

Introduction to Trainspotting

In this post we’ll start looking at the nuts and bolts of making our Caltrain work possible.

Valuing Data is Hard

This article is the first in a series that I will be posting on the topic of thinking about data as an intangible asset, and how to value it as such.



  • TDWI Accelerate Boston 2017

    Boston, MA

    We’ll be in Boston covering a variety of topics—from running agile data teams, to visual storytelling with data. Let us know if you’ll be there, or sign up to receive all our slides.

Past Events


  • UNSTRUCTURED Pop-up Data Science

    Seattle, WA

    Joins SVDS CTO John Akred and other panelists from Amazon,, and eBay for a fireside chat on recommendation engines. Then catch data scientist Chloe Mawer’s talk on figuring out how much your data is actually worth.


  • DataEDGE 2016

    Berkeley, CA

    VP of Data Science Jeffrey Yau, along with Data Scientists Chloe Mawer and Daniel Margala, will be presenting on predicting train delays. See more about our train work here.

  • PyCon 2016

    Portland, OR

    Data Scientist Chloe Mawer will be in Portland giving a presentation about our Caltrain research. Our VP of Data Science, Jeffrey Yau, will also be attending the conference. Be sure to find us and say hi!

    You can find Chloe’s slides here.

  • Data Day Seattle 2016

    Seattle, WA

    Join us as CTO John Akred gives a talk on alternative approaches to valuing data within an organization, and Data Scientist Chloe Mawer demonstrates the power of Jupyter notebooks using a real-world train-detection problem. We’ll also present a tutorial on building data pipelines with Kafka and Spark.

Sign up for our newsletter