My education was focused on the earth sciences (geophysics, carbonate geology) and computer science (data analytics); and previous work experience was geared toward data science and plug-in development for the energy industry. Technical skills: Python (10 years) and R (4 years), with 2 years' experience building data science products using Hadoop and its associated dongles (especially Spark). When I'm not sitting in front of glowing rectangular screens at work, I absolutely adore hiking / kayaking / mountain-biking around Texas' glorious Green Belt; resuscitating Apples; and (poorly!) attempting to play bass guitar. Personal passions are sustainable energy and climate change research; STEM education reform; and empowering local governments via data science https://twitter.com/DynamicWebPaige
My education was focused on the earth sciences (geophysics, carbonate geology) and computer science (data analytics); and previous work experience was geared toward data science and plug-in development for the energy industry. Technical skills: Python (10 years) and R (4 years), with 2 years' experience building data science products using Hadoop and its associated dongles (especially Spark). When I'm not sitting in front of glowing rectangular screens at work, I absolutely adore hiking / kayaking / mountain-biking around Texas' glorious Green Belt; resuscitating Apples; and (poorly!) attempting to play bass guitar. Personal passions are sustainable energy and climate change research; STEM education reform; and empowering local governments via data science https://twitter.com/DynamicWebPaige
Engineering Manager, Google Cloud
Amanda is a an engineering manager with the Developer Relations team at Google Cloud, where she leads the engineering team focusing the developer experience for modern architectures and next-generation compute. She has worked in a breadth of cross-functional roles and engineering disciplines for the last 16 years, including data science, machine learning, complex systems and robotics. She creates projects and programs to make machine learning more approachable, most recently co-authoring the O'Reilly book, Feature Engineering for Machine Learning Principles and Techniques for Data Scientists.
You have data. You have questions. You have computers. Now what? Building data science teams, whether from scratch or from existing resources, can be like a choose-your-own-adventure game.
In this talk, we will lay out a framework for making strategic and tactical decisions when growing data science organizations, including how to better communicate risk and resource cost to business leaders. We will review how to evaluate different organizational methodologies and team structures based on your data objectives and potential for growth. Finally, we will walk through modern data architecture patterns to reduce your team's daily friction and generally make everyone happier.