Why you should join Loft as a Data Scientist – loftbr

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Guilherme Marmerola
Why you should join Loft as a Data Scientist


Want to have this view at the end of your workday?

We have excellent infrastructure and a world-class data engineering team, with whom you’ll work in a close partnership. This means you’ll spend less time cleaning and tidying data and more time performing analysis and driving business decisions. Our stack allows us to use state-of-the-art machine learning models and ship them company-wide with little overhead. We use Databricks, which allows us to run a Spark cluster in large AWS machines. We encourage use of open-source libraries and teams have the autonomy to test models and try new approaches.

We hold several events at our rooftop. In this case, we hosted a talk by brazilian explorer Amyr Klink.

Loft’s goal is to leverage technology and data to disrupt the real estate market. Thus, being data-driven is one of Loft’s core values, so you won’t be hindered by politics and lack of buy-in. If you can use data to convince people of making a decision (which should be what you’re best at), there’s nothing else to stop you from having an impact.

Loft adopts Spotify’s engineering model with Squads, Tribes, Chapters and Guilds. On your daily routine, you’ll work in a Squad, alongside a multidisciplinary team of business analysts, designers and developers. They will complement your skills, and will be your partners at solving a specific problem and shipping the best product possible. Additionally, you’ll be very close to your colleagues at the Data Science Chapter, who will have your back on technical issues. Our team has a mix of backgounds in engineering, statistics, economics and business.

You’ll work with a multidisciplinary team of business analysts, designers and developers.

You’ll solve problems that no one in Brazil (and perhaps around the world) has solved before. We built the first Automatic Valuation Model trained with real transactions in Brazil, and we’re just beginning. Here are some examples of questions you’ll try to answer:

  • What index should we use to bring older transactions values to current values, at the most granular level possible?
  • What should be Loft’s price premium, in order to add liquidity to the market and reduce friction in the process?
  • How do we account for risk in the model, given that price predictions won’t be perfect?
  • How do we take macroeconomic risk into account?
  • How long it takes for an apartment to sell? Is price an important driver?
  • Which apartments should be in our portfolio so we meet our business goals?
  • How to find people that will love our product, and let them know we have the perfect home for them in our portfolio?
A small sample of the scope of your models! Taken from our office.

If this article made sense to you, I strongly advise you to apply to our Data Scientist positions and come build the future of real estate with us. We’re looking forward to it!!!!

Our team at the publication of this article! Come join us!



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