You’d be joining the Pricing Team which sits within the Product organization. We’re responsible for pricing every transaction in the marketplace and for using pricing as a lever to improve customer experience.
Where we are today
We’re a fast-paced team that travels the problem identification → experiment proposal → experimentation → system wide deployment loop in a median time of 2 weeks. It’s not uncommon for us to travel across it within a day or two.
Where we are going
Today, most successful experiments are launched systemwide as an additional pricing policy. But we have 600+ active markets, each of which has unique needs. The future we’re building will allow us to tailor policies to each market using an automated infrastructure. If you join, you’ll be embarking on this journey with us.
As a Data Scientist, you will:
Identify opportunities to improve the marketplace experience using pricing by knitting together data and customer conversations
Iterate on experiment proposals that make first-principles arguments to tackle important problems with creative policies
Deploy and monitor experiments using our Python-based experimentation infrastructure and iterate on our infrastructure on a regular basis
Present the results of implemented policies and their impact on the business to a cross-functional group of executives on a weekly basis and launch them systemwide
Coordinate with cross-functional teams to tackle pricing related issues, including customer concerns
You should apply if:
You pride yourself on your writing. You’ll have to write relatively technical experiment proposals that can be read by a broad audience and regular write-ups for weekly rituals. Your writing must be clear and should be able to survive a high level of scrutiny.
You can analyze a dataset like an archaeologist. We’ll expect you to dive deep into the mechanics of customer behavior and reveal powerful, actionable insights about our marketplace. Folks who excel at this always have 10x more questions to ask. We’ll expect you to learn SQL if you don’t already know it.
You focus on delivering value, fast. You’ll be the CEO of your own work. That means prioritizing ruthlessly to ensure that your work delivers tangible value to customers. A lot of times, this means using answers you already have to deploy customer solutions instead of continuing to analyze.
You know probability theory. You’ll have to think deeply about second order marketplace effects on a regular basis. Thinking about the odds of an event happening and how to change those odds will be a regular exercise. You’ll also have to express those odds logically.
You don’t do incremental work. We have a lot of interesting problems, so it’s easy to find ones that deliver clear, but little value. You’d rather focus on thinking about big problems in entirely new ways than focus on small problems.
You can do the math. We stay away from plug-and-play models if we can’t write the math and explain exactly how they’re working. Our ability to use progressively complex ML models is proportional to our first-principles mathematical understanding of our marketplace.
B.A., M.A. or PhD in a quantitative discipline, like mathematics, computer science, statistics, or engineering, or equivalent quantitative experience.
Deep understanding of probability theory.
1+ years of experience in a high-growth environment is preferred but not required. New graduates are welcome.