Walmart Data Scientist Interview Guide

This guide was written with the help of data scientist interviewers at Walmart.

tl;dr

You know Walmart—the retail conglomerate that prides itself on low prices to help you “live better”. With over 255 million customers weekly and a yearly supply chain of over 100 billion items, Walmart may just be a data scientist’s dream. There’s no shortage of data to analyze across their globally distributed brick-and-mortar and e-commerce stores, and the company is consistently expanding its large, 150-person data science team.

If you choose to wade into the Walmart hiring pool, just know the water is a little murky. While the interview process is largely the same for data science candidates, the nature of the questions you’re asked really depends on your interviewers. In this fluid, semi-unstructured interview cycle, you may (or may not) face case studies and write pseudocode or full code. The specific questions vary, but overall you can expect a mix of technical, business, and behavioral questions.

To join Walmart’s ever-growing data science team, be ready to showcase your problem-solving and critical thinking skills, as well as your business savvy. Interviewers want to see that you not only understand the basics of inventory control, optimization, ML algorithms, and model-building processes but that you can truly analyze these concepts and apply them to complex business problems. You’ll also need to be pretty proficient in coding since you’ll be writing production code if you land an offer.

Prepare for your upcoming interviews with Exponent’s Data Science Interview Course, which features a comprehensive breakdown of popular data scientist interview questions as well as in-depth interview rubrics and answer frameworks.

What does a Walmart Data Scientist do?

Data science teams at Walmart are embedded within larger organizations like Tech or Merchandising, and data scientists at Walmart have a lot of ownership over their role. Unlike most data science roles, Walmart Data Scientists implement their own code in addition to developing and testing ML models and algorithms. That means they own the entire data science process, from ideation to production. But don’t worry, engineers don’t get off scot-free—they’ll help with building out the data pipelines you execute.

As a Walmart Data Scientist, you’ll build and train models and ML algorithms, write and maintain the associated production code, and work directly with end users to continuously improve their experience. You’ll succeed in this role if you see yourself as a business stakeholder rather than just a data optimizer, especially if you can communicate effectively with cross-functional business partners.

As for Walmart’s compensation packages for data scientists? Don’t worry, you’ll definitely live (at least a little) better. Here’s a look at the total compensation averages for Walmart’s most common data scientist levels:

S4 (Senior data scientist): ~$220K
S5 (Staff data scientist): ~$250K
S6 (Principal data scientist): ~$300K

Before you apply

Review your past projects so you’re comfortable contextualizing your optimization and inventory control experience.
Fill in any knowledge gaps around ML concepts and algorithms.
Brush up on your coding skills in case you’re asked to write pseudocode or (less likely) full code.
Take a look at the most popular data scientist interview questions.

Interview process

There might be some deviation between teams, but here’s the closest approximation we can come up with for an outline of a standard Walmart DS loop:

Initial technical screen with an actual data scientist. (That’s right, no recruiter screen with the same basic behavioral questions you’ve answered dozens—if not hundreds—of times before.)
Final round with 4-5 interviews, each targeted toward specific evaluation criteria.

The phone screen is one of the main things that sets Walmart’s data science interview process apart from other companies. Since they cut out the recruiter as a middleman, you’ll hit the ground running in the initial phone call with technical questions from a senior-level data scientist.

Be prepared for inventory control questions even in the initial tech screen. Don’t worry if you’re an entry-level candidate or don’t have IC experience yet; in that case, just make sure you’re familiar with use cases for the major ML models.

The real non-standard portion of the interview is the final round. While there’s a general rubric teams are using to evaluate candidates, a lot of the process boils down to interviewer preferences. For example, coding is important across all data science roles because you’ll be writing production code. However, the way interviewers verify your coding experience varies. Some interviewers just want to see that you can explain your approach, while others want to see you actually write out some code. As long as you’re well-studied and confident, these types of ambiguities shouldn’t shake you up too much.

1. Tech screen

The initial phone screen is your first point of contact with the data science team. A senior data scientist will put your technical knowledge to the test, and they’ll also dig into your past experience. Inventory control and ML models are at the forefront of this discussion.

Any background you might have in production and management operations is helpful here, even if it’s just theoretical knowledge or non-professional knowledge. For ML models, interviewers in both this round and the final round want to see that you’re comfortable explaining the differences between models and that you know when to apply them to different use cases.

Topics to study for this round include:

Inventory control models
Probability distribution of demand
ML algorithm design and selection
ML model-building processes

For most candidates, this is the round that determines their level. Less senior candidates tend to focus on day-to-day tasks and answer questions at a more surface level, while more senior candidates tend to think more strategically and see the bigger picture. For example, staff-level candidates might be able to define single- vs. multi-echelon inventory management, whereas a principal-level candidate tactically applies them to specific case studies.

Tip to avoid downleveling: Domain experience is what sets principals apart from staff data scientists. Walmart Data Science interviewers have noted that lower-level candidates tend to treat their experience like a cookbook (i.e., they try to apply the same recipe to every business problem), while more experienced candidates see the nuances of each problem and craft thoughtful solutions accordingly.

tl;dr: Don’t treat your experience as a cookbook! There’s no single correct recipe in data science.

The questions in the initial tech screen are designed to test your problem-solving and decision-making abilities. Can you determine the best solutions (i.e., models or algorithms) for given use cases?

In this round, you’ll see questions like:

What is the bias-variance tradeoff?
How do you deal with overfitting?
When is it better to use a gradient boosting machine for forecasting instead of a random forest?
When should you use a multi-site, multi-item inventory algorithm instead of a collection of single-site, single-item inventory control algorithms?

2. Final round

The final round combines behavioral, coding, and technical questions over 4-5 interviews. Expect each interview to focus on a specific domain (e.g., strictly behavioral questions) and last 30-45 minutes.

There’s a lot left to the interviewer’s discretion in the final round. Cover all your bases and be ready for both theoretical and practical questions that demonstrate your technical knowledge.

Strong programming and communication skills will elevate you in this round. Make sure you’re able to frame any problems you address as business solutions rather than just optimization solutions. This shows interviewers that you understand how your role as a data scientist works within the larger org you’ll join, and that you can work well with both cross-functional stakeholders and end users. Sometimes there’s a cross-functional interview as part of the final round, but this is more common for principal-level candidates.

Behavioral

Typically, you’ll have two behavioral interviews: one focused on how you work with end users and another focused on your overall team fit. In the interview focused on end users, interviewers assess your analytical skills. Do you truly understand what users want in terms of business needs, or do you just automatically try to translate everything into an algorithm?

In the behavioral interview, expect questions like:

Tell me about a time when you had to explain a complex data concept to a non-technical stakeholder.
Have you ever worked on a project where your data insights contradicted business expectations? How did you handle it?
Describe a time when you had to improve the usability of a data tool or report based on feedback from end users.

The most common mistake candidates make in the end-user interview is that they fail to frame solutions around end users in a way that shows business savvy and UX consideration, and instead just focuses on the technical side.

Coding

The coding interview is a deep dive into the languages you’re familiar with and your comfortability with executing production code. This is the most ambiguous interview you’ll have, as the level of technical difficulty depends solely on the interviewer.

Some interviewers want to see you actually write code, while others just want to ensure you have the theoretical explanation down pat. As you progress, interviewers add in more complex details to see your understanding of data structures and high-performance computing.

Topics to study for the coding interview include:

Programming (any languages you’re already familiar with)
Data structures and algorithms
High-performance computing
ML coding

In the coding interview, you might see questions like:

How would you write code to implement a news vendor algorithm?
What happens if you have a probability distribution that doesn’t have an off-the-shelf function within the programming language you’re working in?
Your code takes 4 hours to run but your SLA is 2 hours. How do you approach this?

Technical interview

In addition to the practical coding interview, you’ll also have a theoretical-focused technical interview on ML algorithms. The goal is to assess your ability to translate optimization theory into real-world business applications.

This is another round where interviewers might ask you to write some pseudocode to verify that you know how specific algorithms work. For example, you may get questions about Lagrange multipliers and other models that can address inventory constraints.

Tip to avoid downleveling: The technical interview is another spot where senior-level candidates easily distinguish themselves from their more junior counterparts.

Principal-level candidates tend to be more creative in their problem-solving, offering solutions that show consideration for engineering teams and end users.

Less senior candidates, on the other hand, tend to rely far more on their theoretical background when proposing solutions.

For the technical interview, you can expect a scenario-based question like:

You can fit 10,000 units of inventory into your virtual warehouse before it overflows. You have 1,000 parts that are all roughly the same size/shape. How would you build a news vendor algorithm around these parameters?

Additional resources

Get expert-level support from Exponent’s Data Scientist coaches.
Study up on your table stake ML algorithm skills.
Review Exponent’s behavioral data scientist interview questions.

FAQs

How should I prepare for a Walmart Data Scientist interview?

Know your ML models and algorithms inside and out, especially as they relate to inventory control. Focus on tree-based algorithms like gradient boosting machines, random forests, and regression trees. Be ready to explain not only what these models are conceptually, but how they can and should be used in application.

Make sure you’re comfortable with your programming skills since writing code will be a key part of your role if you’re hired. Even Walmart’s Distinguished Data Scientists have said they still write code pretty often.

Finally, make sure your communication skills are up to par. You should be able to distill technical concepts into simple business terms for cross-functional teammates and end users.

How much do Walmart Data Scientists make?

The median compensation for Walmart Data Scientists is around $185K, including salary, stocks, and bonuses. Check out Walmart’s level-based compensation packages, which support entry-level data scientists all the way up to distinguished data scientists.

How long is the Walmart Data Scientist interview process?

On average, the interview process for a Walmart Data Scientist takes 4-5 weeks, including the initial tech screen and a final round with 4-5 interviews.