Loft’s mission is to disrupt the real-estate market, transforming painful and bureaucratic processes into simple and delightful experiences. There’s plenty of evidence that we’re going to get there, and a lot of reasons for anyone to accept this challenge: Marcio’s , Renata’s and Chiba’s articles present a lot of solid arguments; Florian and Mate experiences as second-time entrepreneurs speak for themselves; our team and investors mean business, and are really hungry for delivering results.
However, on top of all that, I want to add my view on specifically why you, as a Data Scientist, should join Loft. You and I know that Data Science has yet to fully mature in industry, and while some companies might seem very cool at first, doing data science work in them may be very frustrating.
Therefore, I want to clear things up and show you that Loft isn’t one of these companies, and if you join us, you’ll do meaningful, stimulating data science work that will actually drive business decisions and help Loft achieve its goals.
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.
You’ll have freedom to choose a setup that works for you. That includes choosing between Mac or PC, Windows, Linux or OSX, using a second monitor, etc. You can work at your desk or at our rooftop, with one of the best views in São Paulo. You’ll book meeting rooms seamlessly through Google Calendar, and will have brainstorm sessions using one of many whiteboards across the office. You’ll have fruits, snacks and coffee ready for you anytime.
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 spent a small fortune buying data and partnering with companies that can bring us new data capabilities. We have access to real transaction values (which are very hard to get here in Brazil), which come in the form of a messy and expensive document called Matrícula. We invested a lot of our engineering team’s time to build the stack to process Matrículas and transform them in valuable, structured data ready for analysis. We have a big contract with Spry, a company that works similarly to Amazon’s Mechanical Turk, but in the real world: we pay people to physically go to buildings and collect information about them for analysis.
Florian, Loft co-CEO, even joked that “sometime we’ll need to take losses in some transactions, so the algorithms can learn”. Even though this is a joke and it’s very unlikely to happen, I’m confident that the company’s willing to experiment in order to learn and make the best decisions.
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.
The DS Chapter holds weekly training sessions to explore both foundational and advanced machine learning concepts and algorithms. We also encourage Data Scientists to explore new algorithms, read papers, and have small research projects, staying in touch with the state-of-the-art and sharing knowledge with colleagues. Loft holds several meetups. In particular, we have an event called Tech & Beers (yes, you got that right!) where we already discussed Agile methods, Multi-Armed Bandits, Observability and more. We’re also building a culture of hosting internal hackatons (HackDays) for people to work on a side project outside their squad priorities and solve different problems.
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:
- How to find comparable houses when we have thousands of dimensions to compare them? What criteria should we use?
- 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?
And much more! Loft is just getting started and the possibilities for new business models (and new questions that you can answer using data science!) are endless.
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!!!!
Hope the article helped! If you have any questions, please don’t hesitate contacting me on LinkedIn.