Noelle Sio

Data Science at Pivotal

Principal Data Scientist at Pivotal



Noelle Sio Saldana currently heads the Data Science practice at Pivotal in Santa Monica. She has worked with numerous enterprise and startup companies on their analytics initiatives, presented at several industry conferences, and helped hire and train a global Data Science team. She has expertise in combining disparate data sets (e.g. digital, manual, text) and using predictive analytics to drive both internal processes and end user experiences for companies across industry verticals including Media & Entertainment, Automotive, and non-profit.

Previously, she worked as a researcher at eHarmony and Fox Interactive Media, where she leveraged massive datasets up to the petabyte level for marketing optimization, fraud detection, and ad monetization products. She was among the first contributors to Apache MADlib, an in-database machine learning library. Noelle holds an A.B. From Washington University in St. Louis in Applied Mathematics and Physical Anthropology and a M.S. in Applied Mathematics from Cal Poly Pomona.


The success of a Data Science project is not simply the model fit or the accuracy of its predictions; it is whether those models are being leveraged to make smarter business decisions. Over the past few years, Pivotal’s Data Scientists have experimented with software development methods practiced and taught by their Pivotal Labs counterparts in engineering, design and product management. By reframing Data Science as building software and products instead of research, we found that we reaped similar benefits: shorter and more productive iterations, and clients who actually used the models that we built and skills we taught long after we left.

In this talk, we discuss how we have successfully (and maybe not as successfully) borrowed principles from practices like Lean and Agile to Data Science. Topics include:

  • Minimum Viable Product Models
  • Build-Measure-Learn instead of a silver bullet
  • Pair programming
  • Scrums and retrospectives
  • Practicing empathy instead of elitism
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