Instacart Data Science Interview Questions
More than 50% of American families order grocery via Instacart.
Instacart can predict the realtime availability of 200 million grocery items in US and Canada stores. By the end of 2018, 80% of American households will be able to use Instacart. Their teams made data science interesting by using Deep Learning with Emojis. The tech at Instacart blog shares the interesting experiments and Instacart’s journey through Data Science. After going through their blog, it should become very evident that Instacart has one of the best Data Science teams and Data Science problems for a Data professional to work on.
The interview process is pretty straightforward. It starts with a data challenge, followed by a technical phone interview. After you pass these two, there is a round of technical and culture fit interviews on-site. The interviews are short and targeted and provide you a good insight into the job and the teams you’ll work with at Instacart.
- Space, time and Groceries: Instacart tech
- Instacart Anytime: Data Science at Instacart
- How Instacart delivers on time: Using quantile regression
Data Science Related Interview Questions
- When an item isn’t available, what algorithm should we use to replace it?
- How would you staff the team based on delivery data?
- What other products or revenue opportunities will arise from Instacart’s data?
- Write a script to format data in a text file.
- Estimate the demand and supply
- How might you have optimized parameters for this model differently?
- How would you tune a random forest?
- Given a OLTP system which tracks the sales of items with order processing, returns and shipping, create a data warehouse model to find gross sales, net sales and gross sales by product.
- Given a movie database, identify whether a movie has well defined genre.
- How should we solve our supply / demand problems at Instacart?
Reflecting on the Questions
The data science team at Instacart publishes blog articles regularly Engineering Instacart blog. At Instacart, data drives product decisions which reflects in their questions. The questions are aimed to get information on how you will meld within the existing team and if you can think in terms of the problems they are trying to solve. The scale of the items they catalog is huge. Something as day to day as groceries are looked from the purview of data science is interesting. A good knack to solve problems related to logistics and scale can surely land you a job with the largest grocery catalog in the world!
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The sole motivation of this blog article is to learn about Instacart and its technologies helping people to get into it. All data is sourced from online public sources. I aim to make this a living document, so any updates and suggested changes can always be included. Please provide relevant feedback.