Data Engineer / Data Scientist
We’re one of the fastest growing homeownership companies in America. Why? Because we’re making homeownership simpler, faster — and most importantly, more accessible for all Americans.
By combining smarter technology with a desire to not just change one piece of the journey but the entire makeup of what it’s like to buy and own a home in this country, we’re building things that don’t exist yet.
Better.com by the numbers:
- We fund $600 million in home loans per month
- Nearly $5 billion in loans funded since our inception in 2016
- 2 years running, we’re one of Crain’s “Best Places to work”
- We’re #11 on Fortune’s Best Places to Work in NYC
- And #964 on Inc.’s 2019 “5000 Fastest-Growing Companies”
- We’ve secured over $254 million from our investors to date
- ...and counting
We continue to outpace the industry at every turn. Our backers have helped build some of the most transformative tech and finance companies in history. Kleiner Perkins, Goldman Sachs, IA Ventures, Ally Bank, American Express, Citigroup, Activant Capital, and others have all invested in our vision of redefining the entire home buying journey.
A Better opportunity:
Help us hack a thirteen trillion dollar industry by building a product that will allow more people than the status quo to own a home and build wealth rather than rent for life. Our tech team is small, and you will be a big part of defining the technical direction and culture. We encourage proposals for projects off the beaten path, experimentation with different frameworks and libraries, and doing as you see fit to solve problems. We also offer above-market compensation and equity, as well as full benefits.
Some projects you could be working on:
- Work extremely closely with our product team to understand user behavior and come up with product ideas
- Present conclusions to the executive team that can impact the strategic direction of the company
- Build a lead scoring model to help our customer support team prioritize
- Model the time-lag of conversions using fun math like Gamma distributions
- Design experiments to understand causal impact
- Build web scrapers to track price data for competitors
- Build scalable infrastructure to deploy machine learning models and serve predictions
- Build infrastructure for ingesting data into our data warehouse continuously and as close to real-time as possible
Who you are:
- You have at least 4 years experience writing Python, not limited to small scripts, but also working on larger codebases
- You have a few years experience with SQL
- You have experience working with open-ended questions and have a track record of turning fuzzy problems into actionable data
- You are business-driven, and care about getting to the bottom of how to make a startup successful
- You have an interest in statistics and machine learning
- We do continuous deployment and we ship code 50-100 times every day
- The data stack is all in Python 3.7
- We use TypeScript and Python for services
- Redshift for our data warehouse, Postgres for the production databases
- Kubernetes, for deployment and devops
- AWS for infrastructure, leveraging EC2, S3, Redshift, CloudFront, Route53, and much more
- The tech team is currently 50 engineers but growing quickly
- The data team is 5 people but with the plan to grow it to 25 in the next year
- Erik Bernhardsson (CTO) used to run the data team and the music recommendation team at Spotify. He is the open source author of a few popular projects like Annoy and Luigi and writes a blog about (mostly) data
Things we value:
- Curiosity. Why? How? Repeat.
- Nerdiness. Financial news and trends are fascinating. Seriously.
- Relentlessness. No one here gives up. We try. We fail. We try again.
- Passion. If you don’t get excited about homeownership, mortgages, and real estate, it simply won’t work.
- Smarts: book and street. We have to use all the tools at our disposal to build Better.
- Empathy and Compassion. You understand that people's biggest dreams are in your hands.
- Communication. Can you ask for help or put your hand up when you don’t understand?