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Lowe’s Data Science Interview Questions

Company: Lowe’s Companies, Inc.
Designation: Data Scientist
Year of Experience Required: 0 to 4 years
Technical Expertise: SQL, Python/R, Statistics, Machine Learning, Case Studies
Salary Range: 12LPA – 30LPA

Lowe’s Companies, Inc., headquartered in Mooresville, North Carolina, is a leading American retail company specializing in home improvement. With a vast network of stores across the United States, Canada, and Mexico, Lowe’s is a trusted name in the home improvement industry. If you’re preparing for a Data Science role at Lowe’s, here’s a detailed breakdown of their interview process and the types of questions you can expect.

Lowe’s Data Science Interview Questions

We bring some of the questions asked in a Lowe’s Data Analyst interview. They will give you insight of the selection procedure.

Interview Process

The Lowe’s Data Science 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

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 Round

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

Difficulty of Questions

SQL – 8/10

1) How can you retrieve the latest order placed by each customer?

SELECT customer_id, MAX(order_date) AS latest_order_date
FROM orders
GROUP BY customer_id;

2) How do you find employees who joined the company in the last three months?

SELECT *
FROM employees
WHERE joining_date >= DATE_SUB(CURDATE(), INTERVAL 3 MONTH);

3) How can you identify duplicate records in a table based on an email column?

SELECT email, COUNT(*) AS count
FROM customers
GROUP BY email
HAVING count > 1;

4) How can you find the second-lowest salary from the employees table?

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

5) How can you calculate the total revenue generated for each product?

SELECT product_id, SUM(price * quantity) AS total_revenue
FROM order_details
GROUP BY product_id
ORDER BY total_revenue DESC;

R/Python – 7/10

1) Write a Python function to check if a given integer is a palindrome. A number is a palindrome if it remains the same when reversed.

2) Write a Python function using NumPy to compute the mean, median, and standard deviation of a given array.

3) Write a Python function using Pandas to find the top N highest values in a given column of a DataFrame.

4) Write a Python function to find the second largest number in a given list. If there is no second largest, return None.

5) Write a Python function to detect if a linked list contains a cycle using Floyd’s Tortoise and Hare algorithm.

Statistics/ML – 9/10

1) Derive the expectation for a geometric distributed random variable. What is the use of it?

A geometric distribution models the number of trials before the first success in a sequence of independent Bernoulli trials, where each trial has a success probability p.

The expectation E(X) for a geometric distribution is derived as:

E(X) = Σ (from k=1 to ∞) k * p * (1-p)^(k-1)

Using the sum formula for geometric series, we get:

E(X) = 1/p

Use of Geometric Distribution:

• Failure Rate Analysis: Predicting the number of failures before the first success in a system.
• Queueing Systems: Estimating waiting times.
• Marketing & Sales: Estimating how many calls are needed before a customer makes a purchase.

2)  How would you build and test a metric to compare two user’s ranked lists of movie/TV show preferences?

Since the users’ preferences are ranked, a good metric to compare them is Kendall’s Tau or Spearman’s Rank Correlation.

Steps to Build the Metric:

1. Collect Data: Get the ranked lists from both users.
2. Use Kendall’s Tau: This measures how similar the rankings are by counting the number of agreements and disagreements in order.
3. Normalize the Score: Convert it to a scale of -1 (opposite ranking) to +1 (identical ranking).
4. Test the Metric:
   • Generate sample rankings and check correlation values.
   • Compare results with manually analyzed lists to validate accuracy.

Kendall’s Tau is useful as it works well even if the lists have different lengths.

3) Can you choose a strategy for increasing the number of songs listened by users on an online application? How will you decide the types of playlists to suggest?

Strategy to Increase Songs Listened:

1. Personalized Recommendations: Use machine learning (collaborative filtering) to suggest songs based on user behavior.
2. Autoplay & Smart Playlists: Enable continuous play with AI-curated playlists.
3. Gamification: Reward users for listening to new songs.
4. Dynamic UI Suggestions: Highlight playlists based on mood, trends, or time of day.

Choosing Playlists to Suggest:

• Past Listening History: Suggest genres similar to what the user listens to.
• Trending & New Releases: Mix popular and fresh music.
• User-Generated Playlists: Show playlists from similar users.

This strategy ensures users stay engaged and discover new music.

4) How can we make sure that K-Means output is not sensitive to initialization?

K-Means clustering is sensitive to the initial centroids. To make it more stable:

• Use K-Means++: This algorithm selects better initial centroids to reduce variability.
• Run Multiple Times: Execute K-Means multiple times with different seeds and take the best clustering result.
• Increase Iterations: Allow more iterations so clusters can converge properly.
• Normalize Data: Scaling features reduces the impact of outliers on centroid selection.

By applying these techniques, we get more reliable clustering results.

5) You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?

In a normal distribution, about 68% of the data lies within 1 standard deviation from the mean (or median). Therefore, the missing values affect 68% of the data, and 32% of the data remains unaffected.

Case Study

Problem Statement:

Lowe’s wants to improve its demand forecasting model to ensure the right products are available at the right time while minimizing excess inventory. As a Data Scientist, your task is to analyze historical sales data, seasonal trends, and supply chain factors to optimize inventory levels across different store locations.

Dataset Overview:

You have access to a dataset containing historical sales and inventory data for Lowe’s products across multiple stores. The dataset includes:

• Product_ID – Unique identifier for each product
• Store_ID – Unique identifier for each Lowe’s store
• Category – Product category (e.g., tools, appliances, furniture)
• Sales_Quantity – Number of units sold per day/week
• Price – Selling price of the product
• Stock_Level – Current inventory available in each store
• Supplier_Lead_Time – Average time taken by suppliers to restock the product
• Seasonality_Flag – Indicator of whether the product has seasonal demand
• Customer_Demand_Trend – Historical trend of demand fluctuations
• Promotions_Discounts – Information on past promotional campaigns and their impact on sales

Key Questions to Answer:

1. What factors impact product demand at Lowe’s?

• How do seasonal trends affect sales for different product categories?
• Do pricing and promotions significantly influence demand fluctuations?
• How does regional customer behavior impact inventory needs across stores?

2. How can Lowe’s optimize inventory levels across stores?

• Can Lowe’s use predictive analytics to improve inventory planning?
• Should Lowe’s implement automated restocking models based on demand forecasts?
• How can Lowe’s balance reducing excess stock while preventing stockouts?

3. How can Lowe’s improve demand forecasting accuracy?

• Should Lowe’s integrate weather data, economic indicators, and competitor pricing into its models?
• How can AI and machine learning enhance Lowe’s forecasting methods?
• What strategies can Lowe’s use to handle sudden demand spikes (e.g., during natural disasters)?

Key Insights & Business Recommendations

1. Understanding Demand Patterns

• Seasonal Demand Trends: Products like grills and patio furniture see high demand in summer, while heaters and insulation materials peak in winter. Lowe’s should adjust inventory accordingly.
• Price Sensitivity & Promotions: Sales data shows that discounts on power tools and home appliances lead to short-term sales spikes, requiring better stock planning.
• Regional Variability: Stores in hurricane-prone areas experience increased demand for generators and emergency supplies, requiring location-specific inventory strategies.

2. Optimizing Inventory Management

• AI-Based Restocking Models: Lowe’s can implement machine learning models to predict demand at each store and automate inventory replenishment to prevent overstocking or stockouts.
• Dynamic Safety Stock Levels: Instead of a fixed inventory threshold, Lowe’s should adjust safety stock based on real-time sales velocity and supplier lead times.
• Supply Chain Optimization: By identifying fast-moving products, Lowe’s can negotiate better supplier contracts for quicker restocking and reduced lead times.

3. Improving Forecasting Accuracy

• Incorporating External Data Sources: Lowe’s should integrate weather forecasts, local economic conditions, and housing market trends to refine demand predictions.
• AI-Powered Demand Prediction: Using deep learning techniques, Lowe’s can enhance forecasting accuracy for new product launches and changing consumer trends.
• Real-Time Inventory Tracking: Implementing IoT sensors in warehouses and stores can help track stock levels and prevent inventory mismatches.