Walmart is one of the world’s largest chains of discount department stores and continues to build its online sales platform to complement that huge physical footprint. Its persistent focus on innovation prompted Fast Company to announce it as one of its most innovative retailers in 2024.

You can expect competitive salaries, generous incentives such as stock options and their 401(k) match, and a fascinating range of business problems to work on at Walmart. As Walmart focuses on ramping up online sales while continuing to sell products at bargain prices, data scientists are more in demand than ever to help them optimize pricing, operations, and supply chain, build data architecture, and monitor success metrics.

This comprehensive guide will explore frequently asked Walmart data scientist interview questions and provide strategies for approaching them confidently.

Walmart data science interviews focus heavily on machine learning algorithms, mathematical concepts, and coding skills in Python and SQL. Experience in cloud environments, MLOps practices, and agile methodologies are also often advertised in their job posts, so be sure to read the job description and prepare accordingly.

In this first step, a recruiter assesses your background, experience, and fit for the role. Be prepared to discuss your resume, highlighting projects and accomplishments. Use this opportunity to ask the recruiter questions to understand the role, and be ready with some key points to sell your role-specific skills.

After the initial screening, you’ll be asked to complete a HackerRank take-home assignment. The assignment will comprise SQL and Python coding questions, the latter related to an ML problem like time series forecasting. Expect questions of a medium Leetcode difficulty level.

You can expect around five rounds of technical and behavioral interviews conducted over Zoom calls. These include:

**Technical Interviews**: You’ll meet Walmart data scientists who will conduct 2–3 rounds of around 30 minutes each on statistics, coding, and machine learning. The interview process is guided by the requirements of the role and team, so if you’re up for a position where you’ll be building deep learning models, the ML rounds will focus more on such questions.**Case Study**: You will be given a real-world problem to solve to assess your product sense and practical ML knowledge.**Behavioral Interview**: This round will gauge your soft skills, teamwork, and cultural fit within Walmart. Be prepared to discuss how your values align with Walmart’s corporate culture. Expect one round with a hiring manager and one with a director, although this may vary.

*Here are some tips from the Walmart careers page:*

*Ask questions! This could mean higher-level queries about the role and interview process for your recruiter or more in-depth ones for your interviewers regarding the team and its projects and pain points.**Do your research. Visit a store to understand Walmart better as a company. Read their LinkedIn page and corporate website to review their recent news and business objectives.**Dress for success. Even if you don’t do an on-site interview, wearing a suit can leave a good first impression.*

You will be expected to be technically sound in SQL, Python, machine learning algorithms, and analytical solutions and be able to apply these technical skills to real-life scenarios the company faces, such as inventory management, fraud detection, operational improvements, etc.

It is a good idea to stay updated about the company through its website and LinkedIn page. Also, follow firm-specific and data science-related news to keep abreast of the business problems you may encounter.

For a more in-depth discussion, look through our list below. We’ve hand-picked popular questions that have actually been asked in Walmart’s data science interviews.

We’ll discuss these interview questions in more depth below:

This question is commonly asked to gauge whether you’ll stay long-term with Walmart. It also helps the interviewer determine if your goals align with the opportunities they provide.

**How to Answer**

Your answer should be framed positively, focusing on professional growth and how the new role at Walmart will offer you opportunities that align better with your career objectives. Avoid badmouthing your previous employer.

**Example**

*“I am looking for an opportunity that allows me to engage more deeply with large-scale data sets and advanced machine learning problems, which I see as a core part of the role at Walmart. At my previous job, I greatly enhanced my data analysis skills and now feel ready to apply these in a more impactful way. Walmart’s focus on innovation in retail analytics presents a perfect match for my career goals and interests, especially with projects like optimizing supply chain logistics using real-time analytics.”*

Interviewers will want to know why you chose the data scientist role at Walmart. They want to establish whether you align with the company’s culture and values.

**How to Answer**

Start with what you admire about the company and how it ties with your values and career goals. Demonstrate that you know the company, position, and the work that the team does. The interviewer wants to establish that you aren’t applying randomly for the role and are actively interested in working for the company.

**Tip**: Check the Walmart careers page for pointers on this question. Mirror their language when possible.

**Example**

*“Walmart is at the forefront of integrating advanced data analytics into retail operations, a business problem that I find fascinating. I’m particularly impressed by Walmart’s initiatives in real-time data utilization and AI to enhance supply chain efficiencies and personalize customer interactions. With my background in machine learning and predictive analytics, I see a great opportunity to contribute to these areas.”*

Since Walmart is an equal-opportunity employer focusing on diversity and inclusion, the company would want to understand how well-versed you are in avoiding algorithmic prejudices.

**How to Answer**

Describe an instance where you identified bias in a dataset or analysis process, and highlight the impact of your actions on the project outcomes.

**Example**

*“In a previous project to enhance loan approval algorithms for a fintech company, I looked at historical trends and identified a bias where applicants from certain zip codes were less likely to be approved. We then re-evaluated our data sources and model assumptions and made the approval process more equitable. We did this by incorporating a broader set of financial health indicators and removing zip code as a determinant factor.”*

For Walmart, which operates in a competitive market, having proactive and dedicated employees can make all the difference in maintaining its market leadership.

**How to Answer**

Choose an example from your professional experience where you took additional steps that were not expected in your role but significantly contributed to the project’s success.

Tip: Use the STAR method of storytelling for behavioral interview questions. Discuss the **S**pecific situation you were challenged with, the **T**ask you decided on, the **A**ction you took, and the **R**esult of your efforts.

**Example**

*“In my previous role, I was part of a project to reduce shipping costs for our online marketplace. While our initial objective was to optimize routing algorithms, I proposed we also analyze packaging practices across our distribution centers. By leading a side analysis of packaging data, we identified inefficiencies and proposed new packaging guidelines. This approach ultimately saved the company an additional 10% on shipping costs annually.”*

Real-world data is seldom perfect, and there will be occasions when your team or manager asks for your input when a quick decision is paramount. The interviewer wants to test your domain knowledge and critical thinking skills.

**How to Answer**

Provide an example of a timely decision you had to make with partial data. It’s essential to convey the rationale behind your decision. Also, demonstrate that you are willing to seek expert help when needed—this shows that you are a team player.

**Example**

*“In my old firm, we faced a tight deadline to launch a marketing campaign with incomplete customer data. I looked at existing trends to extrapolate missing information and consulted with domain experts. Based on this, we made an informed decision to proceed with a targeted approach, which ultimately resulted in a successful campaign with higher-than-expected engagement rates.”*

Probability, permutations and combinations, and logical thinking are mathematical skills essential to analyzing retail data.

**How to Answer**

Emphasize the importance of considering all possible combinations of three cards and then the favorable outcomes. Inform the interviewer what mathematical approach (i.e., binomial distribution) you are going to follow.

**Example**

*“The total number of ways to draw three cards from 500 is $^{500}C_3$. Each specific set of three cards can only be arranged in one way to meet the condition (ascending order). So, the probability is the number of sets of three cards, which is $^{500}C_3$ divided by the total number of ways to draw three cards.”*

Forecasting sales is the backbone of Walmart’s operations, and as such, this question tests whether you can add value to Walmart’s existing algorithms.

**How to Answer**

Outline a structured approach to build a forecasting model, mentioning the types of data you would consider, the statistical methods you would implement, and how you would validate your forecasts. Consider seasonal variations, trends, and any external factors that could influence sales. Mentioning edge cases is especially important as it demonstrates your nuanced thinking.

Tip: Always ask clarifying questions to define the scope of the problem.

**Example**

*“I would start by gathering historical sales data alongside external variables such as economic indicators, seasonality factors, and marketing activity data. I would likely use a time series analysis approach, such as ARIMA or exponential smoothing because these models are well-suited for capturing trends and seasonal patterns. I would explore LSTM neural networks if the data shows non-linear patterns. I’d validate the model using back-testing with historical data to ensure its accuracy. This approach can be tailored to different product categories or regional sales data to refine the accuracy of our forecasts.”*

Knowing the applications of each technique in a retail context can help create more sophisticated models.

**How to Answer**

Highlight the key differences and provide relevant examples of when you would employ each method.

**Example**

*“Bagging, like in a random forest, is robust against overfitting and works well with complex datasets. However, it might not perform as well when the underlying model is overly simple. For instance, in a Walmart context, bagging could be ideal for the stable prediction of sales across many product lines where the variance in the model’s predictions needs to be minimized. Boosting, exemplified by algorithms like XGBoost, often achieves higher accuracy but can be prone to overfitting, especially with noisy data. It’s also typically more computationally intensive.”*

Proper stocking is crucial for maximizing sales while minimizing overstock and understock situations. Data science teams work on such problems, particularly for new product launches where initial demand is uncertain.

**How to Answer**

Describe a framework that incorporates market analysis, predictive modeling, and simulation to estimate demand. Mention how you would use historical sales data of similar products, demographic data, and possibly customer surveys or market analysis to inform your model. Also, consider how logistical factors and store profiles would impact your decision.

**Example**

*“I would first analyze sales data of similar products. This includes looking at regional sales trends and demographics to tailor the inventory to local market demands. I would use regression to forecast initial demand based on factors like similar product launches and promotional effectiveness and a clustering approach to categorize stores based on sales volume and customer demographics. I would also propose a pilot launch in a select number of stores to gather real-time sales data that could be used to adjust our models.”*

As a data scientist at Walmart, you’ll need to handle time-series data to analyze activity patterns or financial transactions.

**How to Answer**

Demonstrate your knowledge of SQL aggregate and window functions to partition the data.

**Example**

*“I’d convert the* `created_at`

*timestamp to a date format to group transactions by their respective days. Then, employing the* `ROW_NUMBER()`

*window function, partitioned by the transaction date and ordered by the transaction time in descending order, I’d rank transactions for each day.”*

This question tests your ability to use advanced analytical techniques in practical business applications.

**How to Answer**

Outline the components of an MDP: states, actions, rewards, and transitions. Explain how each component would relate to the context of designing a customer rewards program.

**Example**

*“I would define each state as different levels of customer engagement, such as new, occasional, frequent, and loyal. Actions would represent different types of incentives we could offer, like discounts, loyalty points, or exclusive offers.*

*The transition probabilities between states would be estimated from historical data on how similar actions have influenced past customer behavior. For example, how likely will a new customer become a frequent shopper if offered a 10% discount on their next purchase? The rewards in the MDP model would be defined in terms of business objectives, such as increased customer spending or a higher frequency of visits.*

*To solve the MDP, I would use reinforcement learning to find the optimal policy that maximizes the long-term rewards, which in this context would translate to maximizing customer lifetime value.”*

This question tests your understanding of dimensionality reduction and clustering techniques and how they can be used together to enhance data analysis. It’s relevant for data science roles at Walmart, where you’ll analyze complex datasets to optimize operations and targeting.

**How to Answer**

Discuss the conceptual link between PCA and K-means clustering, emphasizing PCA’s role in reducing dimensionality for more efficient and potentially more accurate clustering by K-means.

**Example**

*“PCA and K-means clustering are often used together in data preprocessing. PCA reduces dimensionality by transforming data into a set of linearly uncorrelated components that retain most of the variations. This simplification can be helpful before applying K-means clustering, as it makes the clustering process more efficient. By focusing on the principal components, K-means has to deal with less noise and fewer irrelevant dimensions, which can lead to more meaningful clusters.”*

`product`

table, write an SQL query to calculate the moving average of daily sales for each product category over the past 7 days.You’ll be thoroughly tested on your SQL knowledge in the Walmart context, particularly joins, window functions, and aggregation functions.

**How to Answer**

Describe the window function you would use that allows calculation of the moving average. Specify the window frame to be the current row and the six preceding rows (representing 7 days). It’s vital to ensure the query handles grouping by product category and ensures proper ordering by date to calculate the moving average accurately.

**Example**

*“I’d ensure each row in the product table includes the total sales for that day. Then, I would use the SQL* `AVG()`

*function combined with the* `OVER()`

*clause to specify a window frame. The query would group results by product category and order them by the date to ensure the calculation reflects the correct sequence of days.*

*The windowing function would be set to include the current row and the six preceding rows, representing the last 7 days. This allows the* `AVG()`

*function to calculate the average sales over this rolling 7-day window for each category.”*

In Walmart’s context, ensemble methods are applicable to various challenges, such as demand forecasting, customer segmentation, and fraud detection.

**How to Answer**

Emphasize the use of bootstrapping and feature randomness. Discuss the advantages of using random forest, contextualizing it to match some of Walmart’s common retail problems.

**Example**

*“Random forest generates its forest by creating multiple decision trees during the training process. Each tree is built from a random sample of the training data, taken with replacement, known as bootstrapping. Random forest introduces randomness while splitting nodes by selecting a random subset of the features to consider at each split. This randomness helps in making the model more robust against overfitting, which is common with single decision trees.*

*We would use a random forest over other algorithms because it reduces variance without substantially increasing bias. This makes it better for complex datasets that have a mix of numerical and categorical data, which is common in retail settings. Random forest can handle overfitting better than many algorithms, especially with high-dimensional data. It also provides feature importance scores, which can be very insightful for understanding which factors most influence the prediction target.”*

This is asked to assess your understanding of data preprocessing and its impact on model accuracy and performance.

**How to Answer**

Focus on how feature scaling aids in faster convergence during training, ensures uniformity in feature influence, and enhances the interpretability of model coefficients. Talk about the practical implications of these benefits.

**Example**

*“Feature scaling standardizes the range of independent variables, leading to faster convergence during optimization. Additionally, it allows for easier interpretation of the model’s coefficients, as each coefficient reflects the relative importance of its corresponding feature in terms of the scaled range. This is particularly useful in a retail context, where understanding the influence of different customer attributes on purchasing decisions, for instance, can aid in more effective targeting and personalization strategies.”*

In the coding rounds, you will be tested on Python skills such as string manipulation, arrays, lists, dictionaries, and common data analysis libraries.

**How to Answer**

Explain your logic clearly, and remember to mention handling edge cases like empty strings or strings of different lengths.

**Example**

*“I would first check if A and B are of equal length, as that is the only scenario where this shift is feasible. Then, I would concatenate A with itself, forming a new string A+A. The logic is that if B is a shifted version of A, then B must be a substring of A+A. For example, if A is ‘abcd’ and B is ‘cdab’, concatenating A with itself gives ‘abcdabcd’, and you can see that B is a substring of this.”*

Walmart’s data scientists need to be adept at assessing the quality of their models, so your grasp of concepts in machine learning and statistical modeling will be thoroughly tested.

**How to Answer**

Before explaining your logic, ask clarifying questions like: What kind of model is it? How many variables are in the design matrix? Be clear on the exact context of the business problem before diving into the solution.

**Example**

*“R-squared measures the proportion of variance in the dependent variable that is predictable from the independent variables. However, it doesn’t provide insight into whether the independent variables are the correct ones for predicting the outcome. Moreover, R-squared always increases as more predictors are added to a model, regardless of whether those variables are meaningful.*

*Another significant limitation is that R-squared does not account for the possibility that a model could be making systematically incorrect predictions even though it explains a lot of variability. For instance, if we are modeling customer spending at Walmart based on the number of visits, an R-squared value might indicate a good fit, but it won’t tell us if we’re missing out on more relevant variables like product prices or seasonal effects that could provide more accurate predictions. Therefore, we’d need to complement R-squared with other metrics like adjusted R-squared, RMSE, or cross-validation results to get a more holistic view of model performance.”*

This question tests your ability to fine-tune pricing algorithms, which are crucial for maintaining competitive advantage and profitability.

**How to Answer**

The expectations for case study questions are often unclear, and this type of problem can be large in scope. Ask your interviewer relevant questions about the business objectives. At the outset, mention your plan and the points you wish to touch upon. After you’ve jotted down your solution, check this video for a detailed solution to the problem.

**Example**

“*My first step would be to conduct a thorough analysis to identify why this is occurring. I’d look at the data inputs used by the algorithm, such as competitor pricing, cost data, demand elasticity, and historical sales. It’s crucial to ensure that the data feeding into the model is accurate, too.*

*I would then review the algorithm’s logic to see if the market data is misinterpreted or if certain variables are weighted incorrectly. For example, if the product is seasonal and the algorithm doesn’t account for seasonality, that could lead to pricing errors.*

*After pinpointing the issue, I would recalibrate the model with adjusted parameters or enhanced data inputs. I would also implement a monitoring phase to evaluate the impact of these changes. If feasible, I’d recommend A/B testing to compare the outcomes of the old and new pricing strategies on a controlled group of products.”*

Understanding the strengths and limitations of various forecasting models is essential for making accurate predictions over different time horizons.

**How to Answer**

Discuss the capabilities of LSTM models in capturing long-term dependencies in sequential data compared to traditional time series forecasting. Make sure you briefly mention the limitations.

**Example**

*“LSTMs are quite effective for forecasting problems where the data has long-term dependencies, as they retain information for long periods. This is useful for demand forecasting, where patterns can span weeks and are influenced by holidays or local events. However, for very long-term forecasting, the performance of LSTMs might not be ideal. This is because the further out we try to predict, the more uncertainty there is, and LSTMs, like any model, can struggle with the accumulation of prediction errors over time.”*

For Walmart, understanding patterns in customer ordering habits by address can help optimize logistics.

**How to Answer**

*Briefly state any assumptions you’re making, such as assuming the* `users`

*table doesn’t have duplicate IDs, while the* `transactions`

*table might contain duplicate IDs. This shows that you are willing to think in-depth and are familiar with common data quality issues.*

**Example**

*“I would join the* `users`

*table with the* `transactions`

*table on the user ID. I would then use a* `CASE`

*statement within a* `SUM`

*function to count the number of times each user’s transaction address matches their primary address versus when it does not. The final step would be to compare these two sums for each user to see if there is a general trend of more orders being placed to the primary address.”*

Understanding the probability of events in games of chance, such as rolling a die, requires a solid grasp of probability theory and geometric series.

**How to Answer**

Highlight the significance of using probability theory to assess the chances of Amy winning. Explain that you’ll approach the problem through both linear equations and geometric series, showcasing your methodical reasoning.

**Example**

*“To find the probability that Amy wins, we first define the events and their respective probabilities. By setting up a system of linear equations, we consider the probability that Amy loses the first roll, transitioning the advantage to Brad. Alternatively, we can express the probability as an infinite geometric series, accounting for the repeated opportunities for both players. The solution through either method reveals that Amy’s probability of winning is ^{6}⁄_{11}”*

Understanding the factors that contribute to variations in machine learning model outcomes is crucial for developing reliable and consistent models.

**How to Answer**

Identify the key sources of variability and explain how each factor can lead to different results, even when using the same algorithm on the same dataset.

**Example**

*“One source of variation is the initialization of parameters, particularly in neural networks, where different starting weights can lead to the model converging to different local minima. For instance, when training a recommendation engine, this variability can result in slightly different recommendations for the same user. Another factor is the use of stochastic methods like data shuffling or stochastic gradient descent, which introduce randomness to enhance the model’s generalization but can also lead to different models. In a retail context, this might mean that sales forecasts vary slightly across different training runs due to these stochastic processes.”*

Here are some tips to help you excel in your interview:

Develop a solid understanding of the retail industry, including supply chain management, inventory management, customer analytics, and e-commerce trends. Research Walmart’s business model, focusing on how they use data to drive decisions, optimize operations, and enhance customer experience. Understanding the company’s culture and goals will allow you to align your responses with what Walmart is looking for in an employee.

Follow their LinkedIn page and read about their hiring process on their website.

Brush up on core data science topics like statistics, machine learning algorithms, data preprocessing, and model evaluation. Be comfortable with Python or R, SQL, and the Python libraries that are commonly used for machine learning and statistical modeling, like pandas, scikit-learn, and TensorFlow.

For further practice, look at our take-home assignments on topics like inventory management and time series forecasting.

*If you need further guidance, we also offer a tailored Data Science Learning Path that covers core topics and practical applications.*

Soft skills such as collaboration and adaptability are paramount to succeeding in any job, especially data science roles, where you’ll need to coordinate with teams from non-technical backgrounds and stakeholders from different geographies.

To test your current preparedness for the interview process, try a mock interview to improve your communication skills.

$133,366

Average Base Salary

$187,725

Average Total Compensation

The average base salary for a data scientist at Walmart is US$133,366, making the position very attractive for prospective applicants.

For more insights into the salary range of data scientists at various companies, check out our comprehensive Data Scientist Salary Guide.

You can apply to similar roles in other retail companies. We have interview guides for Target, Costco, Amazon, and Home Depot.

You can read more on our Company Interview Guides page for insights on other tech jobs.

We have jobs listed for data science roles in Walmart, which you can apply for directly through our job portal. We also have openings at similar organizations, such as Amazon, so you can explore options based on your career goals.

Succeeding in Walmart data scientist interview questions requires a strong foundation in coding and algorithms, the demonstrable ability to apply them to real-world retail problems, and the skill to communicate your findings to business stakeholders.

If you’re considering opportunities at other companies, check out our Company Interview Guides.

You can also explore our interview guides on the data analyst, data engineer, and software engineer positions, which are updated with a 2024 outlook, in our main Walmart interview guide.

With diligent preparation and a solid interview strategy, you can confidently approach the interview and showcase your potential as a valuable employee to Walmart. Check out more of our content here at Interview Query, and we hope you land your dream role soon!