Shopify Data Scientist Interview Guide (2026)
Detailed, specific guidance on the Shopify Data Scientist interview process - with a breakdown of different stages and interview questions asked at each stage
The role of a Shopify Data Scientist
Data science is a crucial part of Shopify’s mission to make commerce better for everyone. Data science at Shopify principally includes two types of work: building products directly backed by data (e.g. machine learning models), and listening to their voice to figure out what to build and how to build it.
Shopify looks more for Data Scientists who have solved challenging problems than those familiar with specific tools or technologies. They are looking for talented data scientists to help them better understand their merchants and buyers.
Shopify hires Data Scientists across the company and there are different seniority levels depending on the scope and expected impact.They have Senior and Staff level roles and some openings for Data Science Managers. For instance, there was recently a role in Merchant Services - the goal being to give small businesses targeted advice that can help them better compete with larger ecommerce players. Given the scope and complexity of this undertaking, this was a Staff level role. You might also want to check the Google Data Scientist and Square Data Scientist guides.
As a Data Scientist, you are expected to play a crucial role pushing forward product development across multiple domains. Data Scientists do this by informing high-level product strategy and execution, building a strong and broad operating picture allowing Shopify to move quickly, and/or improving underlying algorithms, heuristics and rules powering individual products - making the Shopify Data Scientist a sought-after role!
How to Apply for a Data Scientist Job at Shopify?
Take a look at Shopify’s website and visit their careers page. You'll find plenty of opportunities along with the Data Scientist role at Shopify, and you can easily apply to roles directly on the site. However, we highly recommend taking the referral route if you know someone in the company as it significantly increases your chances. Before you hit the apply button, make sure you read the job requirements thoroughly. Nothing's more frustrating than getting caught off guard during an interview. If you want to increase your chances even more, tailor your resume to align it with the qualifications and experiences listed in the job posting. It'll make you stand out from the rest. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.
Responsibilities of a Data Scientist at Shopify
The responsibilities of a data scientist at Shopify across roles can broadly be seen as-
- Actively seeking out and promoting projects that tackle vital issues.
- Utilizing Data Engineering frameworks (such as Starscream and Seamster) to gain insights.
- Being sufficiently familiar with BI tools such as Mode, PowerBI, and Tableau - to be able to visualize data in an effective way to drive insights.
- Collaborating with teams such as product, engineering, and other business leaders to make data-driven decisions.
- Utilizing analytical tools to identify opportunities and guide execution.
- Developing actionable KPIs, production-quality dashboards, informative deep dives, and scalable data products.
- Influencing leadership to drive more data-informed decisions across the organization.
Check out the Microsoft Data Scientist and Uber Data Scientist guides for additional insights.
Skills and Qualifications needed for Data Scientists at Shopify
Here are some skills and qualifications that will help you excel in your Data Science interviews at Shopify.
- It's beneficial to have at least 5+ years of experience in Data Science roles, which can help you stand out from other candidates. From what we’ve seen - candidates with less than 5 years of experience often struggle - even to get interview calls; let alone the interview loop.
- Brush up on your SQL skills and make sure you have a solid understanding of software engineering fundamentals.
- Being proficient in Object Oriented Programming will give you an edge in the interview process. This is because Data Scientists at Shopify are expected to be pretty much “full stack” in nature.
- Showcase your experience with applied statistics and your ability to solve quantitative problems.
- Be prepared to explain how you've turned analytical insights into clear recommendations, and demonstrate your ability to communicate effectively with technical and non-technical stakeholders.
- Highlight your understanding of dimensional modeling and the ability to scale ETL pipelines.
- Having experience launching productionized machine learning models at scale will give you an edge over the other candidates.
- A Domain experience in e-commerce, marketing or SaaS can help to tailor your approach in a specific industry.
Shopify Data Scientist Interview Guide
Shopify handles over $200 billion in GMV annually across millions of merchants in 175+ countries. Their data science team works with massive transactional datasets where even small analytical improvements drive real revenue at global scale. Data role expectations differ across companies. In some organizations, data scientists focus primarily on reporting and analytics. At Shopify, the expectations are higher. Data scientists embed directly with product, engineering, and UX teams and are held accountable to the impact they drive within their product scope.
The interview tests whether you can translate commerce data into merchant outcomes. Prepfully coaches who worked at Shopify told us the process emphasizes causal inference, rigorous experimentation, and business impact over ML algorithm trivia. This guide breaks down the interview structure, technical expectations, and prep strategies based on what Prepfully candidates have shared.
Check out video guide that delves into the interview process and provides valuable tips tailored to each round of the interview.
How the Interview Process Works
The Shopify data scientist interview spans 4-6 weeks with roughly one week between stages. The process has 5-6 rounds, though we've heard from Prepfully candidates that this varies depending on team and level.
Recruiter Screen (20-30 minutes)
Overview
The initial call focuses on background validation and role alignment. Prepfully candidates reported that recruiters assess whether your experience reflects autonomy, speed, and merchant-first thinking, traits central to how Shopify operates. Recruiters are notably transparent about leveling expectations early, though final level determination happens only after technical interviews.
Candidates who connect their background to Shopify's mission by framing past accomplishments in terms of merchant or business impact tend to resonate with recruiters. Prepfully candidates who had worked on checkout flows, retention modeling, or marketplace analytics found these experiences resonated with interviewers, since Shopify's product problems map closely to these domains.
For more practice with retail-focused data science roles, check out the Walmart Data Scientist interview guide which covers similar supply chain analytics and large-scale experimentation approaches.
→ Read NowLife Story Interview (30-60 minutes)
Overview
Shopify often places the behavioral round earlier than most companies, which Prepfully candidates noted caught them off guard. The format differs from typical "tell me about a time" questions. Interviewers walk through your career, asking about pivots, failures, and turning points. They want to understand how you grew through challenges. Keep stories focused on work decisions and professional development rather than personal anecdotes unrelated to your career.
Prepfully coaches who conducted these interviews told us they listen for self-awareness, ownership mentality, and adaptability in fast-moving environments. Prepare 2-3 detailed stories using the STAR structure: situation, action, outcome, and reflection on what you learned. Keep each story to 2-3 minutes, pausing for follow-up questions rather than delivering a monologue. Close each story by connecting the lesson to how you'd contribute at Shopify.
Technical Screen (60-90 minutes)
Overview
The technical round tests problem-solving on live coding exercises and data analysis scenarios that mirror Shopify's work. Focus areas include:
- SQL queries on commerce datasets: window functions, joins, aggregations, date logic
- Python data manipulation: pandas, object-oriented programming, feature engineering
- Statistical reasoning: hypothesis testing, regression concepts, distributions
- Product case analysis: metric definition, funnel analysis, data-driven recommendations
Shopify's engineering blog notes that interviewers prefer "a well approached and considered solution that's incomplete" over "a finished solution that's sloppy, untestable, and fragile." Candidates who narrate their reasoning, validate edge cases, and tie analyses back to merchant outcomes perform well.
Take-Home Assignment (4-6 hours)
Overview
Some candidates receive a take-home challenge involving SQL, Python, and analytics on e-commerce datasets. These exercises require data cleaning, exploratory analysis, feature engineering, and sometimes model building. Submissions typically include a clean, well-documented notebook.
Pair Programming (60-90 minutes)
Overview
Alternatively, candidates participate in pair programming with a Shopify data scientist. According to Shopify's engineering blog, interviewers assess "whether you think out loud, ask clarifying questions, and treat the interviewer as a peer." Prepfully candidates confirmed the format emphasizes collaboration, thinking aloud and asking questions tends to perform better than silent coding.
Product Analytics & Experimentation (60-75 minutes)
Overview
This round simulates day-to-day data science work. Common scenarios include interpreting A/B test results, forecasting growth, designing experiments, segmenting merchants by value, or analyzing metric changes.
Shopify has invested heavily in experimentation infrastructure. Interviewers probe understanding of multiple-comparison corrections, CUPED/covariate adjustment, heterogeneous treatment effects, and guardrail metrics. The depth expected scales with seniority. For Staff-level roles, expect questions on metric risks (bias, noise) and how you'd mitigate them. Prepfully candidates told us that framing problems around merchant conversion or retention, defining clear success metrics, and discussing trade-offs transparently resonated with interviewers.
Final Panel (Half day, 2-3 sessions)
Overview
At this stage, you meet product managers, engineers, data leaders, and potential collaborators. Conversations shift from technical depth to strategic thinking, stakeholder influence, and collaboration. Expect questions about how you've guided teams toward data-driven decisions, handled trade-offs with incomplete information, and navigated disagreements.
Shopify evaluates whether you can thrive in a remote-first, asynchronous environment. Ask clarifying questions, share assumptions explicitly, and summarize discussions before responding.
What Technical Rounds Cover
SQL Expectations
SQL proficiency is essential for data science roles at Shopify. Prepfully candidates tell us SQL appears in multiple rounds.
Prepfully candidates confirmed questions involving:
- Finding the top product pairs most frequently purchased together
- Calculating upsell rates (customers who bought additional products after first purchase, excluding same-day)
- Computing 3-day rolling averages of deposit transactions
- Identifying customers with unfulfilled orders older than 15 days
Interviewers expect understanding of window functions (RANK, ROW_NUMBER, LAG, LEAD), CTEs, correlated subqueries, and performance considerations for billion-row tables. Write comments explaining your logic, validate assumptions about timezones and null handling, and check whether your output makes business sense given Shopify's scale.
Python & Coding
Python questions assess both algorithmic thinking and data science fundamentals. Shopify expects object-oriented programming proficiency since data scientists contribute to production codebases.
Reported question types include:
- Building supervised learning algorithms from scratch without scikit-learn
- Implementing data structures like priority queues
- Computing "alphabet sum" for strings (a=1, z=26) for feature engineering
- Processing time-series data with pandas and calculating summary statistics
The pair programming format evaluates communication as much as code quality. Narrate your approach, test your logic with examples, and discuss complexity trade-offs.
Statistics & Experimentation
Experimentation questions are central to Shopify interviews. Shopify runs experiments at scale, and data scientists are expected to design rigorous tests and interpret results critically.
A question that a couple of recent Prepfully candidates interviewing at Shopify reported: "Your manager ran an A/B test with 20 variants and says one is significant. Would you be skeptical?" This tests fundamental understanding of multiple comparisons and Type I error inflation. Prepfully coaches believe Shopify asks this to confirm candidates can explain statistical concepts to less technical stakeholders.
Ian Whitestone, a former Shopify Data Science Lead, published "10 Lessons Learned From Online Experiments" on Shopify's engineering blog. The article covers sample ratio mismatch troubleshooting, power analysis mistakes, and practical challenges running experiments at scale. We recommend reading this thoroughly since it gives excellent insight into the sort of things Shopify thinks about when running experiments; and these are then no-brainers to reference within an interview.
Other topics Prepfully candidates saw:
- Designing A/B tests for checkout features with primary metrics and guardrails
- Recognizing when you can't randomize and using quasi-experimental methods instead
- Difference-in-differences, propensity score matching, instrumental variables
- Using CUPED to reduce experiment runtime and reach conclusions faster
If you want to strengthen experimentation foundations, Prepfully's Data Science Interview Course covers A/B testing frameworks and causal inference methodologies with a lot of practical examples of how to apply these within the context of an interview, co-created by senior data science leaders from FAANG companies.
→ Start Preparing NowProduct Analytics Cases
Product cases test how well you tie technical analysis back to merchant outcomes. Prepfully coaches told us that Shopify interviewers respond well when candidates frame analyses around merchant-specific metrics like conversion rates, churn indicators, GMV impact, rather than generic engagement numbers. This reflects how Shopify's product teams operate day-to-day.
Prepfully candidates reported these case themes:
Merchant retention analysis: How do you define churn? What leading indicators matter (order frequency, support tickets, feature adoption)? How would you segment cohorts and recommend specific actions?
Checkout conversion optimization: Break down the funnel (visits, cart add, shipping info, payment, order placed). Where are the biggest drop-off points by device and customer type? What experiments would you run, and which metrics would you track?
Shop App engagement: What counts as engagement here? How do different user segments behave? What product changes make sense, and how would you test them?
Fraud detection system design: How do you handle imbalanced classification? What are the latency requirements for real-time scoring? How do you balance false positives vs false negatives? What features would you engineer from transaction data?
Recommendation systems: How would you build app/theme recommendations for merchants? Collaborative filtering, content-based, or something hybrid?
Effective answers tend to follow a consistent structure: what's the business goal, what metrics matter for merchants, how would you analyze it, and what are the limitations. Shopify values insights that drive decisions over perfect analysis that sits in a notebook.
For candidates preparing for product case interviews, the Meta Data Scientist interview guide covers similar experimentation and metrics-focused cases, with comparable emphasis on connecting analysis to business outcomes.
→ Read NowThe Life Story Interview
The life story round appears earlier in Shopify's process than behavioral rounds at most companies. Prepfully candidates noted that interviewers prioritize authenticity and self-reflection over rehearsed responses. They want to understand how you view your own development.
Prepfully candidates shared that effective stories demonstrate:
- Ownership of projects end-to-end, not just task execution
- Learning from failures and changing approach as a result
- Adapting to ambiguity and high-velocity environments
- Connecting lessons to specific ways you'd contribute at Shopify, for example, how a past experimentation failure taught you to set guardrail metrics, which you'd apply to merchant growth analyses
Common questions include:
- "Describe a data project you worked on. What were the challenges?"
- "Tell me about a time your analysis was wrong. What happened?"
- "Describe a disagreement with a teammate on approach. How did you resolve it?"
- "Why Shopify specifically? What about the role excites you?"
Ella Hilal, VP of Data Science and Engineering at Shopify, has discussed hiring values in Forbes and on podcasts. She emphasizes looking for candidates who are "humble, honest, and hardworking" with first-principles thinking and genuine curiosity about solving business problems, not chasing credentials or compensation. Frame your answers around merchant outcomes and learning, not technical showmanship.
Shopify's Data Science Culture
Merchant Obsession
Shopify measures success through merchant outcomes, revenue growth, conversion improvements, reduced churn, rather than consumer engagement metrics alone. Each analysis ties back to merchant value. When discussing conversion rates, consider how checkout friction costs merchants sales. When discussing recommendations, think about how merchants get discovered by buyers. Prepfully coaches told us that candidates who frame analyses around merchant impact tend to resonate with interviewers.
Default to Open
Shopify prioritizes transparency and asynchronous communication. A remote-first environment requires clear assumptions, thorough written documentation, and proactive updates. Candidates comfortable with async-first teams and documentation cultures tend to do well in behavioral rounds.
Digital by Default
Shopify has been a remote-first company since 2020. Work is designed with the assumption that teammates may rarely meet in person, which means thorough documentation, structured onboarding, and flexibility across time zones. For data scientists, this translates to clean notebooks, reproducible analyses, and clear async handoffs.
Technology Stack
Shopify's data infrastructure includes:
- GCP and BigQuery for data warehousing
- Merlin, an internal ML platform built on Ray and Kubernetes for model training and inference
- Pano, a feature store based on Feast for consistent feature definitions and real-time serving
- dbt for analytics engineering and SQL-based data modeling
- Airflow for orchestration
- Looker for self-serve analytics and dashboards
Prepfully candidates who lacked direct experience with some of these tools still succeeded when they demonstrated strong analytical reasoning. Interviewers care more about how you approach problems than whether you've used a specific platform before.
For personalized guidance on Shopify interviews, you can book time with data scientists who have worked at Shopify through Prepfully's coaching network.
→ Book NowHow to Prepare
SQL (Priority: High)
Practice window functions, CTEs, and date logic on commerce-like datasets. Focus on:
- Cohort analysis queries (registration date, 12-month spend by category)
- Rolling averages and running totals
- Multi-table joins on orders, customers, products, merchants
- Edge case handling for nulls, timezones, and refunds
DataLemur and StrataScratch have Shopify-specific SQL questions with complete schemas.
Statistics & Experimentation (Priority: High)
Understand A/B testing beyond the basics:
- Multiple comparison corrections (Bonferroni, Benjamini-Hochberg) to control false discovery rates when testing many variants
- Power analysis and sample size calculation
- CUPED for variance reduction and faster experiment conclusions
- Quasi-experimental methods (difference-in-differences, propensity score matching) for cases where randomization isn't possible
Read Shopify's engineering blog post on quasi-experiments and counterfactuals for context on their causal inference approach.
Domain Knowledge (Priority: Medium)
Build familiarity with Shopify's ecosystem:
- Create a trial store to understand the merchant dashboard
- Explore the Shopify App Store and themes marketplace
- Understand core metrics: GMV, conversion rate, AOV, CLV, churn
- Read Shopify's engineering blog for context on data infrastructure
Domain knowledge is often the differentiator. It signals genuine interest to interviewers and helps you align answers to how Shopify teams think about problems internally.
Behavioral Preparation (Priority: High)
Given how heavily Shopify weighs the Life Story interview, behavioral preparation deserves significant focus. Prepare 2-3 detailed stories demonstrating:
- End-to-end project ownership with measurable business outcomes
- A time you failed, what you learned, and how you changed your approach
- Communicating technical findings to non-technical stakeholders
- Navigating disagreement with a teammate constructively
For each story, close by connecting the lesson to how you'd apply it at Shopify. Be specific about merchant outcomes or product problems you'd address.
Practicing under time pressure with feedback accelerates preparation. Book a mock interview with a Prepfully coach to practice case studies, behavioral questions, and pair programming scenarios.
→ Book NowCompensation Ranges
Shopify pay varies by level and location. These numbers come from levels.fyi and are approximate, compensation changes over time.
Canada (CAD):
- L4 (Data Scientist): CA$86K - CA$120K
- L5 (Senior): CA$116K - CA$150K
- L6 (Staff): CA$146K - CA$180K
- L7 (Principal): CA$177K - CA$220K
United States (USD):
- L5 (Senior): $180K - $200K
- L6 (Staff): $208K - $250K
- L7 (Principal): $245K - $310K
RSUs follow a 3-year vesting schedule with 33% in year one, then quarterly vesting. Stock becomes increasingly important at senior levels, representing 15-20% of total compensation at L6+.
Applicants share that Shopify is a place where people are leveling when compared to FAANG. Experience may be used to match at other companies to L4 and Staff-equivalent experience to L5 sometimes. This has an impact on negotiating compensation particularly in leverage with competing offers. Shopify will not match FAANG compensation pound-for-pound, but remote-first culture and upside on equity are distinguishing factors.
The Shopify Data Scientist Interview tl;dr
Experimentation depth matters. Shopify invests heavily in experimentation, so Prepfully candidates reported questions on experiment design, statistical corrections, and interpreting results. The depth expected varies by seniority, but understanding core concepts like power analysis and guardrail metrics helps at all levels.
The Life Story interview happens earlier than at most companies. While resume-based questions can still come up, the format leans toward walking through your career to assess self-awareness, ownership, and adaptability. Prepare 2-3 detailed stories that connect what you've learned to specific ways you'd contribute at Shopify.
SQL proficiency is essential. Window functions, CTEs, and commerce data logic (cohorts, upsell rates, rolling averages) come up in multiple rounds. Prepfully candidates told us that writing clean, commented code matters more than clever solutions.
Prepfully candidates reported that Shopify levels conservatively compared to FAANG. Experienced candidates sometimes start at lower levels than expected. Keep this in mind for compensation negotiations. Shopify's 3-year vesting schedule with 33% in year one is more front-loaded than many competitors with 4-year schedules.
The interview reflects remote-first culture throughout. Pair programming tests collaboration more than speed. Interviewers observe how you communicate and document your thinking. Treat each conversation as practice for how you'd work day-to-day.
Ready to prepare for your Shopify Data Scientist interview? Book a mock interview with Prepfully coaches to get feedback on experimentation cases, SQL optimization, and pair programming scenarios before the real thing.
→ Book NowRecently reported Shopify Data Scientist interview questions
Would you walk me through your approach for calculating the rolling average of a metric over a time span?
Can you explain why decision trees might be preferred over other machine learning techniques in certain situations, and vice versa?