Dollar General is a major American retail chain that offers a wide selection of household items, groceries, and general merchandise at low prices. With a mission to support communities and improve lives through cost-effective solutions, the company continues to expand its presence across the United States.

As a Data Scientist at Dollar General, you will play a crucial role within the Decision Science & Analytics team. The position involves developing and executing customer and marketing analytics programs, building predictive models, and driving insights from transactional data. You will work on a variety of projects, including automating analytics processes, creating customer segmentations, and conducting deep dive analyses. The ideal candidate will be proficient in statistical and machine learning techniques, data manipulation, and visualization tools like SQL, Python, PowerBI, and Tableau.

This guide by Interview Query provides an in-depth look into the interview process, expectations, and key questions for the Data Scientist role at Dollar General. Let's get you prepared!

Submitting Your Application

The first step in securing a Data Scientist role at Dollar General is to submit a compelling application that reflects your technical skills and interest in joining the company. Whether you are directly contacted by a recruiter or apply independently, carefully review the job description and tailor your CV to match the listed requirements.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and work experiences that align with the role.

Recruiter/Hiring Manager Call Screening

If your CV is shortlisted, a recruiter from the Dollar General Talent Acquisition Team will contact you to verify key details about your experiences and skill level. This initial call may also include behavioral questions to better understand your fit for the role.

In some cases, the hiring manager for the data scientist role might also be present during this screening call to answer any questions you have about the position and the company. They may also touch upon surface-level technical and behavioral discussions.

The recruiter call generally lasts around 30 minutes.

Technical Virtual Interview

If you navigate the recruiter round successfully, you will be invited to a technical screening round. This stage is typically conducted virtually and involves video conferencing and screen sharing.

Questions in this 1-hour-long interview may revolve around Dollar General’s data systems, ETL pipelines, and SQL queries. You may also be assigned take-home assignments covering product metrics, analytics, and data visualization. Your proficiency in hypothesis testing, probability distributions, and machine learning fundamentals could also be assessed.

For roles with greater seniority, additional case studies and real-scenario problems might be part of the evaluation.

Onsite Interview Rounds

Following a second recruiter call which outlines the next steps, you’ll be invited for onsite interview rounds. These consist of multiple interview sessions, each designed to evaluate your technical expertise, including programming and machine learning modeling capabilities.

If you were assigned any take-home exercises, you might be required to present your findings during the onsite interview. These sessions can involve live coding tests, whiteboard problems, and in-depth discussions about your past projects and experiences.

The entire onsite interview process aims to assess how you stack up against the other finalized candidates and typically lasts a full day.

Quick Tips For Dollar General Data Scientist Interviews

To excel in your Dollar General Data Scientist interviews, make sure to:

• Brush Up on Analytical Platforms: Dollars General uses distributed compute platforms like Databricks, Hadoop, and Snowflake. Ensure you are familiar with these systems and can discuss your experience proficiently.
• Master Data Storytelling: You will need to present complex data insights to non-technical stakeholders. Practice explaining technical concepts clearly and concisely.
• Immerse in Machine Learning Techniques: Dollar General places significant emphasis on predictive modeling and machine learning. Brush up on common modeling techniques and machine learning libraries to demonstrate your expertise.

Sign Up at Interview Query

Typically, interviews at Dollar General vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• Create a function recurring_char to find the first recurring character in a string. Given a string, write a function recurring_char to find its first recurring character. Return None if there is no recurring character. Treat upper and lower case letters as distinct characters. Assume the input string includes no spaces.

• Write a query to get the average order value by gender. Given three tables representing customer transactions and customer attributes, write a query to get the average order value by gender. Round your answer to two decimal places.

• Identify first-time and repeat purchases by product category. Analyze a user's purchases to identify which purchases represent the first time the user has bought a product from its category and which represent repeat purchases. Output a table including every purchase with a boolean column indicating if it’s a repeat purchase.

• Parse the most frequent words used in poems. Given a list of strings called sentences, return a dictionary of the frequency that words are used in the poem. Process all words as lowercase and ignore punctuation marks.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, select the next highest salary.

• What would you do if friend requests are down 10% on Facebook? A product manager at Facebook informs you that friend requests have decreased by 10%. How would you approach diagnosing and addressing this issue?

• How would you set up an A/B test for changes in a sign-up funnel? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What metrics would you use to determine the value of each marketing channel? Given all the different marketing channels and their respective costs at a company called Mode, which sells B2B analytics dashboards, what metrics would you use to assess the value of each channel?

• How would you measure the success of a banner ad strategy for an online media company? An online media company wants to experiment with adding web banners into the middle of its reading content to monetize effectively. How would you measure the success of this banner ad strategy?

• How would you investigate a drop in posts per user on Facebook? The posting tool on Facebook composer drops from 3% posts per user last month to 2.5% posts per user today. How would you investigate this issue? If the drop is in photo posts, what would you investigate next?

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

• What is the difference between covariance and correlation? Provide an example. Describe the difference between covariance and correlation, and provide an example to illustrate the distinction.

• What are time series models? Why do we need them when we have less complicated regression models? Explain what time series models are and why they are necessary despite the availability of simpler regression models.

• How would you determine if the difference between this month and the previous month in a time series dataset is significant? Given a time series dataset grouped monthly for the past five years, describe how you would assess if the difference between this month and the previous month is significant.

• How would you address a manager's complaint about a packet filling machine not functioning correctly? A manager reports that a packet filling machine, which aims to place 25 packets into a box, is malfunctioning. Customers are complaining about incorrect packet counts. How would you investigate and resolve this issue?

• How does random forest generate the forest and why use it over logistic regression? Explain the process of generating a forest in random forest and discuss the advantages of using random forest over logistic regression.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Describe how you would justify the complexity of a neural network model for solving a business problem and how you would explain its predictions to non-technical stakeholders.

• How would you interpret coefficients of logistic regression for categorical and boolean variables? Explain the interpretation of logistic regression coefficients for categorical and boolean variables.

• Which model would perform better for predicting Airbnb booking prices: linear regression or random forest regression? Compare the performance of linear regression and random forest regression for predicting booking prices on Airbnb and explain which model would likely perform better and why.

• What are the assumptions of linear regression? List and explain the key assumptions underlying linear regression.

What is the average salary for a Data Scientist at Dollar General?

Q: What role does a Data Scientist play at Dollar General?

A: A Data Scientist at Dollar General leads the development and execution of customer and marketing analytics programs. This role involves working with internal resources and third parties to analyze transactional data, develop predictive models, create customer segmentations, and provide actionable insights for marketing strategies. Additionally, projects include automating analytics processes and performing ad-hoc deep dives into category performance.

Q: What qualifications are required for a Data Scientist position at Dollar General?

A: Candidates should have a Master’s degree (or equivalent) in a quantitative field like Data Science, Statistics, Economics, Computer Science, or Mathematics. A minimum of 2 years of hands-on industry experience is preferred. Proficiency in SQL, Python, PySpark, machine learning libraries, data preparation, and feature engineering techniques is essential, along with experience in large data manipulation and using analytical platforms like Databricks, Hadoop, or Snowflake.

Q: What types of projects will I work on as a Data Scientist at Dollar General?

A: Projects include developing predictive and deterministic models, automating analytics processes, and creating customer segmentations. You will also conduct large-scale hypothesis testing, classification, prediction, and develop recommender systems. Additionally, you will create dynamic, scalable models that deliver ROI and perform comprehensive data analysis tasks from data gathering to visualization.

Q: What skills and tools are crucial for a Data Scientist role at Dollar General?

A: Key skills include strong problem-solving abilities, business judgment, and robust quantitative analysis. Proficiency in SQL, Python, PySpark, machine learning models (such as logistic regression, decision trees, and random forests), and data visualization tools like PowerBI and Tableau is required. Experience with large data handling, statistical/machine learning projects, analytical platforms (Databricks, Hadoop, Snowflake), and code management tools such as GitHub is crucial for this role.

Q: What is the work environment like for a Data Scientist at Dollar General?

A: The role offers a hybrid work model permitting telecommuting up to 3 days a week from a home office within normal commuting distance of Goodlettsville, TN. The environment encourages collaboration with IT resources, third parties, and the Decision Science team to drive data-driven decision-making and innovation. The work is dynamic, balancing between analytical tasks and stakeholder engagement.

Pursue your dream job at Dollar General. Prepare with Interview Query!

Embark on your journey to becoming a Data Scientist at Dollar General, where you'll lead the development and execution of cutting-edge analytics and machine learning projects. This role offers the perfect mix of dynamic team collaboration, advanced statistical modeling, and impactful data storytelling. If you want more insights about the company, check out our main Dollar General Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other data roles, where you can learn more about Dollar General’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Dollar General Data Scientist interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!