Shopify Interview Questions — Page 10

Shopify·Data Scientist·Technical Phone Screen

Jun 2026

Shopify data scientist interview with a time-series case study. The whole thing was basically one big open-ended scenario about explaining anomalies in shopping session data and then doing some hands-on exploration. Felt more like a product analytics exercise than a traditional DS interview.

You're shown three years of weekly Shopify session data. There's a spike every November and a one-off spike in June of year three. What business reasons could explain each of those patterns?
Using a dataset they provide, dig into those spikes further and come up with at least two hypotheses you'd want to validate with more data.

“The November part was easy, Black Friday and Cyber Monday basically write themselves.”

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Shopify·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026

Interviewed for a PMM role at Shopify. Just one question to go off here but it's a meaty one about positioning validation.

How did you know your positioning was actually working?

“This tripped me up more than I expected.”

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Shopify·Data Scientist·Technical Phone Screen

Jun 2026

Shopify data scientist interview that centers almost entirely on one deep technical project walkthrough. They want the full picture: problem, data messiness, modeling choices, experiment design, deployment, and what you'd change. Be ready to go several layers deep on any part of it.

Walk me through a technical project you led or meaningfully contributed to in data science, analytics, ML, or engineering. Start with the big picture, but be ready to go deep on any part of it.

“This is basically the whole interview.”

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Shopify·Data Scientist·Technical Phone Screen

Jun 2026

Shopify data scientist interview with a meaty case question about piracy detection metrics. One question, but it had a lot of layers and I felt like I was being tested on whether I'd actually worked with messy real-world data before.

You have two preliminary findings: monthly pirated-theme usage appears to rise from 0% to 100% over the observed period, and cumulative estimated revenue loss keeps growing. A PM asks if this is a red flag and what to do next. How do you present these results, which metrics do you lead with, and what caveats and follow-up analyses would you flag before the PM commits resources?

“This one is deceptively wide.”

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Shopify·Software Engineer·Technical Phone Screen

Jun 2026

Shopify coding interview that extended an existing Mars rover simulation. The core task was adding collision detection to a multi-rover system, which sounds contained but has more edge cases than you'd expect going in.

You're given a Mars rover controller that already handles creating, deleting, selecting, and moving rovers. Add collision avoidance: if the cell directly ahead of the selected rover is occupied by another rover, the move command should fail and the caller should be notified. Nothing else about the rover's state should change.

“The base system being pre-built was a small relief, but I spent too long re-reading the existing API instead of just jumping into the collision logic.”

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Shopify·Machine Learning Engineer·Recruiter / HR Screen

Jun 2026

Shopify ML Engineer screen, mostly motivational stuff. Three questions back to back about why Shopify, why my major, why this role now. Felt like a recruiter call more than anything technical.

Why are you interested in Shopify specifically? What about their mission, products, or culture connects with you, and how does it fit your background and where you want to go?
Walk me through your academic path: why did you pick your undergrad major, what pushed you toward grad school, and why are you going after this ML engineering role now?

“I had an answer prepped but it came out a bit generic.”

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Shopify·Machine Learning Engineer·Technical Phone Screen

Jun 2026

ML modeling round at Shopify for an MLE role, focused entirely on building an ETA prediction system for package delivery. Pretty deep dive, they wanted you to actually think through the full pipeline not just name-drop algorithms.

How would you design an ML system to predict package delivery time?
What features would you engineer for a delivery time prediction model?
How would you handle in-flight packages when training your model? What does censored data mean in this context?
Which model would you choose as a baseline and when would you consider something more complex?
What metrics would you use to evaluate the model, both technical and business-facing?
How would you productionize this model and what would you monitor post-deployment?

“This is basically a full system design question dressed up as a modeling question.”

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Shopify·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Shopify ML Engineer interview with a grid simulation problem. Pretty straightforward coding round but there were enough edge cases to trip you up if you weren't careful about the boundary conditions.

A robot navigates a 2D grid using a sequence of instructions (turn left, turn right, move forward, move backward). Given the grid dimensions, starting position, and starting direction, simulate the robot's movement and return its final position and orientation. Instructions that would move the robot off the grid are ignored.

“The core logic isn't hard but I spent way too long second-guessing the direction rotation math.”

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Shopify·Data Scientist·Technical Phone Screen

Jun 2026

A data science round at Shopify focused entirely on communicating analytical results about pirated themes to a product manager. The question was less about the math and more about how you'd structure a short readout and what decisions you'd actually be driving toward.

You've calculated the monthly share of shops using pirated themes and estimated revenue loss from those themes (monthly and cumulative). Walk through how you'd present these findings to a Product Manager in a 5 to 10 minute readout, covering your headline, which visuals you'd lead with versus keep as backup, the assumptions behind your revenue estimate, how you'd handle data quality issues or weird patterns in the data, and what concrete next steps you'd recommend.

“This tripped me up more than I expected.”

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Shopify·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Pair-programming screen at Shopify for an ML Engineer role, focused entirely on building a tiny URL shortener from scratch in memory. No ML content at all, which threw me a bit, but the follow-up questions made it clear they cared more about systems thinking than the initial implementation.

Implement an in-memory URL shortener with two methods: one that takes a long URL and returns a shortened version under a fixed host, and one that takes the short URL and returns the original. The same long URL should always map to the same short URL within a single process run.
How would you handle collisions if you used a hash-based token instead of an incrementing ID, and what are the trade-offs between the two approaches?
How would you extend this to handle concurrent requests safely?
What would change if you needed to persist the mappings across process restarts?
How would you prevent the short URLs from being guessable or enumerable?
How would you handle invalid inputs, such as a malformed long URL passed to shorten or a short URL that doesn't exist passed to expand?

“Started with an incrementing ID approach because it was the simplest thing I could explain clearly.”

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