Shopify Data Scientist Interview Guide

TL;DR the Shopify DS interview loop has 5 main stages:

Step 1: Recruiter screen

Step 2: Hiring manager meeting

Step 3: Life story

Step 4: Coding Exercise

Step 5: Onsite

• 5.1 SQL pair programming
• 5.2 Python pair programming
• 5.3 Technical deep dive
• 5.4 Case study/data interpretation

As per commenters on Blind, data science work at Shopify is 50% analytics and BI, 35% data engineering, and 15% data products and modeling.

Tobi Lütke built Shopify because he was trying to sell snowboards online and the existing platforms were terrible, so he made his own. Shopify’s origin defines its philosophy: a platform that builds and works for the merchant. 

Lütke shared in a shareholder letter: “We do not chase revenue as the primary driver of our business.” Shopify delivers simplicity. And simplicity is not simple, their business is built to take the complexity of online commerce and make it simple and accessible for businesses of all sizes.  When you interview for a data scientist role at Shopify, keep that in mind.

Shopify's DS loop is fully remote and has five stages. You’ll be on CoderPad for the coding rounds, and Google Meet for everything else. 

The interview process is consistent for teams across the company, but the online case study round will focus on team-specific work. Be sure to ask your recruiter for more detail about the teams that are hiring and the kind of work they do.

The first recruiter call will be a standard conversation about the open role(s) and how relevant your background and skills are. 

Some questions you might be asked:

• Tell me about your background and what you're looking for in your next role. 
• Why Shopify? 
• What are your comp expectations?
• What kind of work do you find exciting? 

Because your ‘Life Story’ is such a huge part of the interview process for Shopify, their recruiters will be closely scrutinizing your personal motivations and how you narrate your experience. They want to know if you’re the kind of person Shopify is looking for.

The HM meeting is common for many roles but not universal, some candidates will go straight to the Life Story or coding exercise after the recruiter call. Hiring managers will want to see recent work examples and prototypes, specifically things that demonstrated high craft. Come ready to show your past work.

The HM is assessing you and also selling you on the team. Expect questions about your experience working with data in a product context, and how you work with engineers and PMs.

Questions reported by DS candidates:

• Why Shopify? Why this role? 
• Tell me about a time you used data to solve a business problem. 
• How do you prioritize when you have competing tasks from multiple stakeholders? 
• How have you influenced a product or business decision without direct authority? 

We recommend you write down some interesting questions you have about the team and role.

The Life Story is a 60-minute conversation about your life and career. Shopify recruiters or hiring managers are usually the interviewers.

Shopify describes the Life Story round as "an organic, two-sided conversation rather than your typical interview Q&A." You will either go through this round before or during your onsite.

The structure is a little loose on purpose so you can’t prepare for specific questions beforehand. You have to prepare a story. Shopify wants someone who's passionate - about work, yes, but also other things in their life - and can connect their life experiences to their interests.

Some Shopify Life Story questions reported by DS candidates:

• Tell me about yourself. What's your story, how did you get here?
• What are you most proud of about your life so far? 
• What’s your favorite thing to do outside of work?
• What are you curious about?

Now, this round can be quite subjective. Many candidates on Blind have said they were cut here despite strong technical rounds. One candidate asked for feedback and the recruiter's response was to send a link to Shopify's values page. Current Shopify employees say you shouldn’t be afraid to be vulnerable when speaking about yourself.

While Shopify interviewers will look for and try to connect things to the work, the interview is all about how well rounded you are as an individual outside of work. You can be as personal as you want, discuss things you enjoy and care about or have taken the time to improve upon. 

The final screen before the onsite loop is a 40-minute coding exercise on CoderPad. 

You’ll be working on one problem with a Shopify dev in a shared environment. Usually it’s an array or string manipulation task. The interviewer is your partner, and will be there to give you feedback and answer any questions you have.

Some candidates might also be given broken code and will have to describe what it should do, find and fix the error, and add unit tests.

Shopify data science candidates should expect questions like:

• Given an array of points where points[i] = [xi, yi] represents a point on the X-Y plane, return the maximum number of points that lie on the same straight line.
• Given two vectors of integers v1 and v2, implement an iterator to return their elements alternately.
• Given an m x n matrix and an integer k, return the max sum of a rectangle in the matrix such that its sum is no larger than k.

Shopify is looking for people who can reason even under uncertainty. While writing a working and optimal solution is important, they also want someone who works fast. 

If applicable, we suggest you ask your partner if they want you to brute force a quick solution first and then find the optimal one or take your time and write the optimal solution. Be sure to talk through the process and discuss how you’re going to make the code more efficient or how you’re accounting for any corner cases.

The Shopify onsite is remote, with around four to six interview rounds. Every interviewer submits an independent assessment. Shopify is a merchant-obsessed company and we recommend you connect to the business/merchant context whenever you’re thinking through problems.

You’re the main driver and your partner will be observing and working through the problem with you.

You will be asked a bunch of SQL questions in the beginning and will also have to implement a Python function.

Alternatively, some candidates at Shopify can have two separate pair programming interviews, one for SQL, and one for Python. Each round could take between 60 to 90 mins.

For the SQL pair programming portion: You’ll be given a Shopify dataset and interviewers will ask ~5 questions that get progressively harder. 

For instance, a few candidates have reported the first couple of questions requiring simple aggregations and/or RANK(); the following questions involved rolling averages, joining multiple tables, CTEs, and subqueries.

For the Python pair programming portion:  You’ll be working in your local environment with your screen shared. 

The most common problem types are ‘design [X] class with OOP framing.’

Or similar to the coding screen, you will be given some broken or sub-optimal code. This time you might have to scour through a codebase. Your job is to describe what the code should be doing, what the problem is, and then find and fix the error. Interviewers will also ask you to add unit tests.

Questions reported by Shopify DS candidates:

• Conway’s Game of Life: given an array of arrays representing the board state (‘x’ for alive and '' for “dead”), compute the next state.
• Can you rank merchants by their Gross Merchandise Volume (GMV) in the first week of March using window functions?
• A merchant tracks price changes for each product variant across periodic update dates. Write a query that shows each update record alongside the difference between the current price and the previous price.
• Design a stack-like data structure to push elements to the stack and pop the most frequent element from the stack.
• Design and implement a data structure for a Least Frequently Used (LFU) cache with get(key) and put(key, value) running in O(1) average time complexity.

A senior data scientist at Shopify who coaches here at Prepfully says:

This is an hour long conversational interview about your past work. 

Come with a couple of past projects you know well, and brush up on the details. We recommend you avoid solo side projects, because Shopify values stories where you deal with multiple stakeholders.

The evaluation criteria for this round can be found on Shopify’s engineering blog.

These questions have been reported by Shopify DS candidates:

• What did you train these models on and how did you assemble their results?
• Why did you pick neural network modeling for churn prediction? Did you try tree based methods? 
• Why did you decide to do supervised fine-tuning on the last few layers versus LoRA?
• What were the technical trade-offs you had to make? Help me understand the context of why you went with what you did.
• If an important confounder was unobserved, how would that have affected your conclusions?

A Prepfully coach who is a senior Applied Scientist says:

For some teams there will be a standalone case study round, while for others you’ll be given a mini case in a technical deep dive round.

The format varies; the most common task is a more hands-on data case where you will be shown a dashboard or some figures and will have to interpret the data or do an A/B test to evaluate the results.

You could also be given an open-ended scenario and you have to define the problem yourself and then propose a path forward. In this latter format, the exercise will be end-to-end. You’ll be given merchant data and will have to analyze it in Python while the interviewer will ask you questions. 

These prompts and follow-up questions have been reported by Shopify DS candidates:

• Can you look for anomalies in our user session data? Please also plot and ID when the anomaly starts. And what do you think is driving it? Also do a statistical test to support or reject it.
• Can you explain this visualization to me?
• Our checkout-to-order completion rate dropped 15% over the past two weeks. Form a hypothesis and investigate.
• Would you go with pointwise, pairwise, or listwise ranking?

Total comp for Shopify data scientists ranges from $151K at L4 to $332K at L7. Base range is $136K to $272K. All numbers are from Levels.fyi.

Shopify has a ‘Flex Comp’ model, meaning candidates receive a total allocation and they can choose how they want to split it into base salary, RSUs and stock options. Employees are also allowed to rebalance their allocation from time to time.

Shopify RSUs vest on a three-year schedule, you get 33% in year one. Then 8.25% quarterly for years two and three.