Procter & Gamble (P&G) is a globally renowned consumer goods company with a legacy spanning over 180 years. Renowned for its innovation and leadership, P&G offers a dynamic workplace with iconic brands that touch the lives of billions every day.

The Data Scientist position at P&G is designed for individuals with a passion for data and analytics. The role involves leveraging advanced machine learning techniques to provide deep insights and drive business decisions across various domains like media, digital commerce, and supply chain. Candidates must demonstrate leadership, technical prowess, and the ability to translate complex data into actionable business strategies.

In this guide, Interview Query will walk you through the interview process, common questions, and tips to help you prepare for a Data Scientist role at P&G. Let's dive in!

Submitting Your Application

The first step in your journey to becoming a Data Scientist at Procter & Gamble (P&G) is to submit a compelling application that highlights your technical skills and showcases your interest in the role. Carefully review the job description and tailor your resume and cover letter to reflect the listed prerequisites, emphasizing your relevant experience and expertise.

Online Assessment

Upon submitting your application, you will receive an immediate email link to an online assessment. This assessment consists of a combination of IQ tests, personality and logic tests, and a series of visual memory tasks. You will need to remember the positions of colored dots on a screen within a few seconds. This evaluation, though challenging and seemingly unrelated to data science, tests cognitive agility and memory retention. Be prepared and stay focused.

Recruiter/Hiring Manager Call Screening

If you pass the online assessment, a recruiter from P&G will reach out to you for a call screening. This initial conversation, lasting about 30 minutes, will cover key details such as your experiences and skills. Behavioral questions will be posed to understand how you handle various work situations and your typical problem-solving approach.

In certain instances, your technical manager might join the call to discuss the role and expect some surface-level technical and behavioral questions.

Technical Virtual Interview

Upon successfully navigating the recruiter round, you will be invited to the technical screening round. This interview, usually conducted via virtual means, spans about 1 hour. The focus here will be on your proficiency in areas such as data systems, ETL pipelines, and SQL queries.

For data scientist roles, expect take-home assignments involving data analysis, modeling, and possibly questions on machine learning fundamentals. Your knowledge of probability distributions, statistical analysis, and data visualization will also be tested.

Onsite Interview Rounds

After clearing the technical round and a second call with the recruiter, you will proceed to the onsite interview loop. This consists of multiple interview rounds, including detailed discussions and problem-solving sessions with various team members. Depending on your performance in earlier rounds, you might also need to present a project you’ve worked on.

You will face questions and situational scenarios to evaluate your decision-making, leadership skills, and technical prowess. Behavioral questions will gauge how well you can integrate into P&G's collaborative culture.

Quick Tips For Procter & Gamble Data Scientist Interviews

• Understand P&G’s Core Values: Demonstrate your alignment with P&G’s principles of leadership, innovation, and citizenship. Showcase how your experience reflects their values.
• Prepare for Behavioral Questions: Be ready to discuss past experiences, particularly situations where you led projects, resolved conflicts, or showcased innovation in your work.
• Practice Cognitive Skills: Given the emphasis on cognitive assessments, practice similar memory and logic tasks to stay sharp and perform well during the tests.

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

• What are the Z and t-tests, and when should you use each? Explain what Z and t-tests are, their uses, the differences between them, and the scenarios in which one should be used over the other.

• What are the drawbacks of the given student test score data layouts, and how would you reformat them? Analyze the provided student test score datasets, identify their drawbacks, suggest formatting changes for better analysis, and describe common problems in "messy" datasets.

• What metrics would you use to determine the value of each marketing channel? Given the marketing channels and their costs for a company selling B2B analytics dashboards, identify the metrics you would use to evaluate the value of each marketing channel.

• How would you determine the next partner card using customer spending data? With access to customer spending data, describe the process you would use to identify the best partner for a new partner card, similar to Starbucks or Whole Foods chase credit cards.

• How would you investigate if the redesigned email campaign led to the increase in conversion rates? Given the increase in new-user to customer conversion rates after a redesigned email journey, explain how you would determine if the increase was due to the new campaign or other factors.

• How does random forest generate the forest and why use it over logistic regression? Explain how random forest generates multiple decision trees and combines their results. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• When would you use a bagging algorithm versus a boosting algorithm? Compare the use cases for bagging and boosting algorithms. Provide examples of tradeoffs, such as bagging reducing variance and boosting improving accuracy but being more prone to overfitting.

• What kind of model did the co-worker develop for loan approval? Identify the type of model used for loan approval. Discuss how to compare it with another model predicting loan defaults, including metrics to track, such as accuracy, precision, recall, and ROC-AUC.

• What’s the difference between Lasso and Ridge Regression? Describe the differences between Lasso and Ridge Regression, focusing on their regularization techniques. Explain how Lasso performs feature selection by shrinking coefficients to zero, while Ridge shrinks coefficients but keeps all features.

• What are the key differences between classification models and regression models? Outline the main differences between classification and regression models. Highlight that classification models predict categorical outcomes, while regression models predict continuous outcomes. Discuss examples and typical use cases for each.

• Write a function search_list to check if a target value is in a linked list. Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with keys value and next. If the linked list is empty, you'll receive None.

• Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product. Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

• Create a function digit_accumulator to sum every digit in a string representing a floating-point number. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

• Develop a function to parse the most frequent words used in poems. You're hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

• Write a function rectangle_overlap to determine if two rectangles overlap. You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

• How would you design a function to detect anomalies in univariate and bivariate datasets? If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

• What are the drawbacks of the given student test score data layouts? Assume you have data on student test scores in two layouts. What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in "messy" datasets.

• What is the expected churn rate in March for customers who bought subscriptions since January 1st? You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers who bought the product since January 1st?

• How would you explain a p-value to a non-technical person? Explain what a p-value is in simple terms to someone who is not technical.

• What are Z and t-tests, and when should you use each? Describe what Z and t-tests are, their uses, differences, and when to use one over the other.

What is the average salary for a Data Scientist at Procter & Gamble?

Q: What is the interview process at Procter & Gamble for a Data Scientist position?

The interview process typically includes an initial phone screen with HR, followed by technical and behavioral interviews with managers, and a final panel interview. You might also need to complete various assessments, including psychotechnical tests, personality assessments, and coding exercises.

Q: What kind of skills and qualifications are Procter & Gamble looking for in a Data Scientist?

They look for strong quantitative and modeling skills, experience with data science tools (e.g., Python, SQL), and a solid understanding of machine learning algorithms. Additionally, demonstrated leadership, problem-solving abilities, and effective communication are key.

Q: How does Procter & Gamble assess leadership skills during the interview process?

Leadership skills are evaluated through behavioral questions, situational scenarios, and past experiences. Candidates are expected to showcase their ability in communication, decision-making, and teamwork effectively.

Q: What types of projects will a Data Scientist work on at Procter & Gamble?

Data Scientists at P&G work on a wide range of projects, including media and marketing optimization, supply chain refining, and digital commerce enhancements. They also may lead cross-functional teams and develop innovative data science solutions.

Q: How can I prepare for a Procter & Gamble Data Scientist interview?

To prepare, research P&G’s latest projects and technologies, practice common interview questions on Interview Query, and refine your coding and problem-solving skills. Showcase your past project experiences and leadership abilities effectively.

Navigating the intricate interview process for a Data Scientist position at Procter & Gamble can be daunting. With a mix of cognitive assessments, behavioral questionnaires, and technical challenges, each step is designed to gauge your comprehensive skill set. Despite the high hurdles and occasional frustrations, those who demonstrate strong leadership, effective teamwork, and robust analytical abilities stand a good chance of advancing.

For a smoother journey, check out our main Procter & Gamble Interview Guide, where we cover many interview questions and offer invaluable insights into the process. Additionally, Interview Query offers interview guides for other roles, such as software engineer and data analyst, giving a comprehensive overview of the interview landscape at Procter & Gamble.

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 Procter & Gamble 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!