Data Minds Episode #8 — Eli Bressert

Interview with Eli Bressert (Manager of Data Engineering and Analytics at Netflix)

You Will Learn:

• How Eli transitioned from academia to applied data science
• Stories from Eli’s time at Stitch Fix (when the data team grew from 6 to 80)
• Advice for newly minted data scientists (overcoming impostor syndrome, thinking as a unit, the importance of a “North Star,” etc.)
• How data teams are structured and what Eli’s team does at Netflix
• How to conduct better data science interviews with Eli’s Four tiered approach
• The importance of a diverse background in data science
• Three skills that make for a great data scientist, but are hard to quantify
• How to look inward and discern if you should play on a team or go lone wolf
• Eli’s purpose in life
• AND much more :)

Meet Eli Bressert

Eli Berssert started his career in the tech field where hefirst worked for the Chandra Space Telescope at the Harvard-Smithsonian Institute. He made many stellar images for Chandra, Spitzer, and Hubble. His Python career started there where I wrote two well-known and highly used astro packages called APLpy and PyFITS. They are legacy today and have been incorporated into AstroPy, the go to tool for astronomy today.

After, Eli went to the UK to do a PhD in astrophysics while writing his PhD thesis; at the same time, he wrote the NumPy and SciPy book for O’Reilly.

From there, Eli went to Silicon Valley, did the Insight Data Science program and worked at Jawbone as a data scientist. Then he went to Stitch Fix to start and lead the R&D data science division. A community builder at heart, Eli also jumpstarted and directed the tech-branding effort at Stitch Fix, such as the MultiThreaded blog, the MultiThreaded meetups and more.

Currently, Eli leads the Growth and Messaging Analytics team at Netflix. We focus on what the title says, but also other functions as well (can share more during interview).

Eli is an advisor for Singularity University where he helps mentor young start ups in regards to data engineering and data science.

Here are a few past talks by Eli:
- At Kaizen Data: Innovating the Next Generation of Data Products
- At UCSD for Brad Voytek’s group: Data Science in the Rough

Eli is a polyglot in regards to languages for data science and will use anything to get the job done. His favorite languages to do data science are Julia, R, Python, and he is currently diving into Scala/Spark.

My current focus is on leveraging data science/analysis to push forward products. The field of data science is still young, but Eli sees some maturity where tool details are becoming less important and simply using the technology to push forward business is key.

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