In the last three months of 2020, we increased the range of homes Opendoor purchases by 35 percent, increasing our coverage of neighborhoods, home type, price points and home age. And to date in 2021, we’ve launched nine new markets and continue to expand our pricing capabilities to serve more sellers.

To be able to accurately price the incredible diversity of homes in America requires scalable pricing models and immense investment in data science. …

After months on the waiting list, I recently received access to GPT-3, OpenAI’s powerful multipurpose language model. GPT-3 was released in June 2020 and there’s been a wealth of writing since then about it’s technical breakthroughs, shortcomings, and potential societal impact. (Here are two pieces I found particularly interesting and insightful.) Since it’s well-trod territory, I won’t comment on the technical aspects of GPT-3 beyond noting the creativity and cogency of its responses FAR outperformed my expectations.

Instead, this post discusses the GPT-3 beta from a product management perspective. Not only is GPT-3 a groundbreaking model, it’s also a very…

In this post, we discuss what “decision products” are and detail three lessons we’ve learned at Opendoor about building and using machine-learning-powered decision products:

  1. Know your user and your data — they’re different
  2. Nail the northstar metric that aligns your algorithm with your business
  3. Regularly reconsider the rules of the game for each decision product

What are decision products?

A decision product is a product that uses an algorithm to recommend or make a decision that helps users. For example, what content will a user be most interested in seeing (e.g., a social network newsfeed)? Or how should we price this service to balance…

My first exposure to OKRs was not love at first sight. Far from it. I thought they were too complex, too rigid, too bureaucratic. What about just getting smart people together and letting magic happen? But over the years, I’ve been converted. Now, I’m a true believer in OKRs.

Done well, OKRs can drive success across all functions, for companies small and large. I’ve found them particularly useful for product teams, where cross-team dependencies are complex and focusing on the wrong metrics can be disastrous. …

You may find this useful if you’re interested in:

  • Automating interactions with 3rd-party websites that lack APIs
  • Managing recurring jobs in a web app
  • Developing with Selenium, Django, or Heroku

The story behind Booking Bot

I love tennis and every week I try to play at the same time and place on San Francisco public courts. In April 2019, SF Parks & Rec closed the Golden Gate Park tennis complex for 2 years for renovations; the closure of these 20 courts tipped the supply/demand balance and it became much harder to find an available public court.

Consequently, SF Parks & Rec released an online tennis court booking system. Each day at 8am, courts become…

This post uses simulations and animations to understand epidemics. I started going down this path in my own attempt to understand the forces driving this crazy new world of coronavirus and “shelter in place.”

I’m hopeful that the underlying model and visualization code may be useful to others, including people (like me) just trying to make sense of what’s going on. I’m also hopeful it may be useful, or at least thought-provoking, for those making policy and healthcare decisions.

A few insights:

  • It’s hard to reduce the fatality rate (deaths / infections) via public policy; it’s easier to reduce the…

All models are wrong, but some are useful. — George Box

Silicon Valley can learn a lot from the data scientists that came decades before the tech industry coined the term “data science.” There are some particularly rich lessons from two quantitative investing firms, both famous but for very different reasons: Renaissance Capital and Long Term Capital Management (“LTCM”). Since launching its flagship fund in 1988, Renaissance has achieved 39% average annual gains over 30+ years and is going strong today. Take your pick of famous investors — Berkshire Hathaway, Bridgewater, Blackstone — Renaissance outperforms all of them by a…

Product managers, myself included, face increasing pressure to integrate machine learning into their products. This pressure comes from many places: engineers and data scientists excited by cutting-edge algorithms, sales and marketing stakeholders looking to solve problems faster and more cheaply, our own desire to serve users better.

As a field, product is in the early days of using machine learning (ML) to power critical user-facing applications. After working on ML applications across real estate, recruiting, and genomics, I’ve found the following to be useful when considering the impact of ML on user-facing products:

  • Distinguishing ML models from rules-based models
  • Recognizing…

Nearly a year ago, we decided to shut down Ansaro. In the immediate aftermath, I wrote a long list of contributing reasons. Then I put it in a drawer for a year; it was too painful to think about. I recently came back to that list, to draw out some lessons, which I hope will be useful for myself and others.

Let’s set the stage… Ansaro’s mission was to improve hiring, via data science-driven SaaS. After 2 years, we’d had a team of a half dozen, $3M in funding, and a handful of Fortune 500 customers. …

Photo Credit — Andrew Malone

Good interviews are like puzzle pieces. Each interview shows a different aspect of the candidate. And like a puzzle, having the right pieces is necessary, but insufficient — you still need to know how to put them together. In other words, a great process, for translating feedback into decisions, is key to great interviewing.

This is the last in our 3 part series on Great Interviewing:

We believe that reference calls are an indispensable piece of the puzzle. But why do them at all? Critics…

Sam Stone

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