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Walmart Labs Data Analyst Interview Questions

Company: Walmart Labs
Position: Data Analyst
Year of Experience Required: 0 to 4 years
Technical Expertise: SQL, Python/R, Statistics, Machine Learning, Case Studies
Salary Range: 12LPA – 30LPA

Walmart Labs, a subsidiary of Walmart, is a technology powerhouse based in San Bruno, California. Known for its innovative e-commerce solutions and data-driven strategies, Walmart Labs plays a crucial role in enhancing Walmart’s online presence. If you’re preparing for a Data Analyst role at Walmart Labs, here’s a detailed breakdown of their interview process and the types of questions you can expect.

Walmart Labs Data Analyst Interview Questions

The following article comprises of a set of selected Walmart Labs Data Science Interview Questions.

Interview Process

The Walmart Labs Data Analyst interview process typically consists of 5 rounds, each designed to evaluate different aspects of your technical and analytical skills:

Round 1 – Telephonic Screening

Focus: Basic understanding of Data Science concepts, SQL, and Python/R.
Format: You’ll be asked to explain your projects and solve a few coding or SQL problems.

Round 2 – Walk-in/Face-to-Face Technical Round

Focus: Advanced SQL, coding, and problem-solving.
Format: You’ll solve problems on a whiteboard or shared document.

Round 3 – Project Analysis

Focus: Deep dive into your past projects.
Format: You’ll be asked to explain your approach, tools used, and the impact of your work.

Round 4 – Case Studies and Statistics

Focus: Business problem-solving and data-driven decision-making.
Format: You’ll be given a real-world scenario and asked to propose solutions.

Round 5 – Hiring Manager + Project Discussion

Focus: Cultural fit, communication skills, and long-term career goals.
Format: Behavioral questions and high-level discussions about your experience.

The difficulty level of Questions

SQL – 9/10

1) How can you fetch the last five orders placed from the orders table?

SELECT *
FROM orders
ORDER BY order_date DESC
LIMIT 5;

2) How can you retrieve employees whose salary is higher than their manager’s salary?

SELECT e.name, e.salary, m.name AS manager_name, m.salary AS manager_salary
FROM employees e
JOIN employees m ON e.manager_id = m.id
WHERE e.salary > m.salary;

3) How do you find the second-highest salary from the employees table?

SELECT DISTINCT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;

4) How can you get a list of products that have been ordered more than 100 times?

SELECT product_id, SUM(quantity) AS total_quantity
FROM orders
GROUP BY product_id
HAVING total_quantity > 100;

5) How do you find customers who have made at least one purchase in the last 30 days?

SELECT DISTINCT customer_id
FROM orders
WHERE order_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY);

R/Python – 7/10

1) Given two dictionaries, dict1 and dict2, create a ChainMap that combines them and access a value from the combined view.

2) Use pathlib to create a Path object for a file and print its name and parent directory.

3) Create a Python function that accepts either an integer or a string as input and prints the input value, using typing.Union.

4) Create a weak reference to an object and check if the object is still alive.

5) Use contextlib.suppress to suppress a FileNotFoundError when trying to open a file that may not exist.

Statistics/ML

1) Which one would likely perform better—Linear Regression or Random Forest Regression? Why?

The choice between Linear Regression and Random Forest Regression depends on the nature of the data.

• Linear Regression performs well when data has a linear relationship and is free from complex interactions or non-linearity.
• Random Forest Regression is better when data has non-linear relationships, interactions between features, or missing values.

Random Forest generally performs better when dealing with real-world datasets because:

• captures non-linearity automatically.
• It is robust to outliers and missing values.
• reduces overfitting by averaging multiple decision trees.

However, if the dataset is small and linear, Linear Regression might be the better choice due to its simplicity and interpretability.

2) Write a function that takes in a list of dictionaries with a key and a list of integers and returns a dictionary with the standard deviation of each list.

To calculate the standard deviation, we follow these steps:

1. Compute the mean of the list.
2. Find the differences from the mean and square them.
3. Compute the average of these squared differences.
4. Take the square root of the result.

Since you don’t want to use NumPy, the function can be written using basic Python operations.

3) What will you do if removing missing values from a dataset causes bias? Is there any other alternative for the same?

Removing missing values can cause bias if the missing data is not random (e.g., missing values are more common for a certain group).

Alternatives to removing missing data:

1. Imputation – Fill missing values using:
   • Mean/Median for numerical data.
   • Mode/Frequent category for categorical data.
2. Predictive modeling – Use machine learning models like KNN Imputation to estimate missing values.
3. Using an indicator variable – Create a new feature showing whether data was missing, which can sometimes improve model accuracy.

Choosing the right method depends on data distribution and the reason for missing values.

4) If two predictors are highly correlated, what is the effect on the coefficients in logistic regression?

When two predictors are highly correlated in logistic regression, it causes:

1. Multicollinearity – The model struggles to determine which predictor is more important.
2. Unstable coefficients – Small changes in data lead to large fluctuations in coefficient values.
3. High variance – The model may overfit, leading to poor performance on new data.
4. Difficulty in interpretation – Coefficients do not represent true relationships.

Solution:

• Use Variance Inflation Factor (VIF) to detect multicollinearity.
• Remove one of the correlated predictors or combine them using dimensionality reduction (PCA).

5) What is a table called if it has neither a Clustered nor a Non-clustered Index? What is it used for?

A table without any index is called a Heap Table.

Uses of Heap Tables:

• They provide fast insert operations, as there is no indexing overhead.
• Useful for staging tables where data is temporarily stored before processing.
• Used when scanning all records is more efficient than indexed lookups.

However, query performance may be slow, so indexes should be added for frequent lookups.

Case Study

Problem Statement:

Walmart wants to optimize its inventory management system to reduce stockouts (out-of-stock situations) and prevent excess inventory that leads to increased storage costs. As a Data Analyst, your task is to analyze past sales trends, demand patterns, and supply chain data to recommend an effective inventory replenishment strategy.

Dataset Overview:

You have access to a dataset containing historical sales and inventory data from Walmart stores. The dataset includes:

• Date – The transaction date
• Store_ID – The unique identifier for the Walmart store
• Product_ID – The unique identifier for the product
• Category – The category of the product (e.g., groceries, electronics, clothing)
• Units_Sold – Number of units sold on a given day
• Stock_Available – Number of units in stock before restocking
• Restock_Quantity – Number of units added during replenishment
• Supplier_Lead_Time – The number of days taken by suppliers to deliver stock
• Seasonality_Factor – Indicator of seasonal demand (e.g., high demand during festivals or back-to-school season)
• Discount_Applied – Any discounts or promotions applied to the product
• Customer_Demand_Forecast – Predicted demand based on historical data and trends.

Key Questions to Answer:

1. What factors influence stock availability in Walmart stores?

• Does demand fluctuate based on the season or specific events?
• How does supplier lead time affect inventory levels?
• What impact do promotions and discounts have on stock depletion?

2. How can Walmart reduce stockouts while minimizing excess inventory?

• Should Walmart adjust restocking frequency for high-demand products?
• Can predictive analytics improve demand forecasting?
• How can Walmart optimize supplier partnerships for better replenishment timing?

3. What strategies can Walmart use to optimize inventory management?

• Should Walmart adopt an automated replenishment system?
• How can Walmart reduce waste in perishable product categories?
• Can Walmart improve customer satisfaction by ensuring product availability without overstocking?

Key Insights & Business Recommendations

1. Identifying Key Inventory Trends

• Seasonal Demand Fluctuations: Certain products (e.g., school supplies, holiday decorations) see sharp demand spikes. Adjusting stock levels based on seasonal trends can reduce stockouts.
• Supplier Lead Time Impact: Stores with longer supplier lead times are more prone to stockouts. Establishing backup suppliers can improve inventory availability.
• Discount-Driven Sales Surges: Products under promotions experience rapid depletion. Walmart should increase stock levels before major promotions to avoid shortages.

2. Improving Demand Forecasting

• AI-Powered Demand Prediction: Using historical sales data and external factors (e.g., weather, economic trends) can improve demand forecasting accuracy.
• Real-Time Inventory Monitoring: Implementing IoT-based sensors in stores can track stock levels and automatically trigger restocking.
• Dynamic Pricing Adjustments: If excess inventory is detected, Walmart can reduce prices dynamically to clear stock efficiently.

3. Optimizing Restocking Strategies

• Automated Inventory Replenishment: Implementing a data-driven restocking model based on sales trends and supplier lead times can reduce manual inventory errors.
• Warehouse Optimization: Redistributing inventory from low-demand stores to high-demand stores can balance stock levels across locations.
• Customer-Centric Stock Allocation: Prioritizing inventory distribution based on store-specific demand patterns ensures high-demand stores receive priority restocking.

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