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Allie Hotzfeld


Allie Hotzfeld headshot

Allie has done many things in her life, from wholesale account management for a bakery to serving as a coordinator and curriculum developer at an outdoor education nonprofit. But along the way she discovered she could apply her love of mathematics through econometrics and analytics. To up her game, she earned her master’s degree in ecological economics at The University of Edinburgh, a choice that both pushed her out of her comfort zone geographically (she’s from Nashville originally), and expanded her horizons through a program boasting a largely international student base. Now with EMI Consulting, she is eager to get her hands on the AMI data available in the utility industry start plugging them into R. She believes that data and analytics will be key in helping people to understand the inherent value of sustainability and clean energy. Wrestling with large data sets to discover meaningful findings is just one way she will contribute at EMI to a better future for all.


knitting projects per year on average


perfected pie crust recipe


bones broken (knock on wood)

  • What is your favorite spot in the world?

    The Vennel in Edinburgh. It’s this flight of stairs that cuts between buildings, and where it widens, there is a captivating view of Edinburgh Castle. I felt at peace there, and while in grad school, I used to go to hang out, journal, and relax.

  • What are the problems you like to solve?

    I can get pretty energetic about finding ways to make use of raw data—I love taking a mess and finding ways to organize it into something that makes sense and tells a story.

  • What is it like working with you?

    I am a terribly affirming person to work with. I check in constantly with teammates and try to make sure everyone feels supported. It’s important to me that everyone feel confident in their abilities and proud of the unique perspectives they contribute.

  • When do you have the most fun at work?

    Coding is pretty gratifying to me because you can know with certainty when it’s correct. If I spend time data cleaning and writing code, there’s (almost) no better feeling than when the analysis runs successfully with no errors.