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What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Shopify.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Shopify during technical interviews.

Analyze Causes of November and June Shopify Traffic Spikes

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's skills in time-series analysis, web analytics, segmentation, causal inference, and data-quality investigation applied to traffic anomalies on an e-commerce site.

Shopify

Aug 4, 2025, 10:55 AM

Data Scientist

Onsite

Analytics & Experimentation

Analyzing Recurring and One-off Spikes in Weekly Shopify Sessions

Scenario

You have a three-year weekly time-series of Shopify shopping sessions. The plot shows:

A pronounced spike every November.
An additional isolated spike in June of the third year.

Assume you have access to a dataset of weekly sessions with a date field, and optionally common marketing dimensions (e.g., country/region, channel/source, device, campaign/UTM). If the dataset is daily, you can aggregate to weekly.

Tasks

Explain plausible business drivers for the recurring November spikes and the one-off June spike.
Using the dataset (choose Python, R, or Google Sheets), outline how you would explore the spikes and propose at least two concrete hypotheses to validate with additional data.

Hints

Consider: seasonality (e.g., Black Friday/Cyber Monday), marketing campaigns, external events, segmentation by geography or channel, product or tracking changes, and data quality checks.

Solution

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