Johnson & Johnson Data Scientist Interview Questions + Guide in 2024

At Johnson & Johnson, their commitment to health innovation empowers them to deliver groundbreaking solutions, prevent complex diseases, and enhance lives globally. Renowned for being at the forefront of healthcare innovation, they focus on developing smarter, less invasive treatments and personalized healthcare solutions.

This guide will navigate you through the interview process, frequently asked Johnson & Johnson data scientist interview questions, and provide essential tips to help you succeed. Let’s dive in!

The interview process usually depends on the role and seniority, however, you can expect the following on a Johnson & Johnson Data Scientist interview:

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Johnson & Johnson Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Johnson & Johnson Data Scientist hiring manager might be present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Johnson & Johnson Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around technical knowledge, projects, and past experiences.

You may also face questions that assess your proficiency in: - Basic statistics. - Technical knowledge related to deep learning and image segmentation. - Practical applications of AI-type algorithms.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. This stage often includes multiple rounds such as: - Detailed discussions on your resume. - Questions about your work experience, technical questions, and I/O deep learning models. - Leadership meetings and one-on-one sessions with senior company leaders, focusing on problem-solving skills and understanding past data science projects from end to end.

In certain situations, you might be asked to give a presentation based on your past projects or dissertation work, elaborating on the specifics and answering detailed questions.

Final Evaluation and Outcome

After clearing the onsite interview rounds, the final evaluation might be conducted by the HR and sometimes the VP of relevant departments. Despite the comprehensive interview process, be mindful that there could be extended gaps between different rounds, and it’s not uncommon to experience delays in feedback or outcome emails.

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

1. Would you suspect anything unusual about the A/B test results with 20 variants?

Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

2. What considerations should be made when testing hundreds of hypotheses with many t-tests?

You are conducting multiple hypothesis tests using t-tests. What factors should you take into account to ensure the validity of your results?

3. How would you generate and evaluate a daily report for advertiser campaigns and impressions?

Given a schema with advertiser campaigns and impressions, generate a daily report for the first 7 days. Evaluate each campaign’s delivery and identify which promos need attention using specific heuristics.

4. How would you determine if a redesigned email campaign led to an increase in conversion rates?

A new marketing manager redesigned the new-user email journey, and conversion rates increased from 40% to 43%. However, the rate was previously 45% before dropping to 40%. How would you investigate if the redesign caused the increase?

5. What analysis would you conduct to improve the UI of a community forum app?

You have access to tables summarizing user event data for a community forum app. What kind of user journey analysis would you perform to recommend UI changes?

6. Create a function max_substring to find the maximal substring shared by two strings.

Given two strings, string1 and string2, write a function max_substring to return the maximal substring shared by both strings. If there are multiple max substrings with the same length, return any one of them.

7. Develop a function moving_window to find the moving window average of a list of numbers.

Given a list of numbers nums and an integer window_size, write a function moving_window to find the moving window average.

8. Write a function to determine if a string is a palindrome.

Given a string, write a function to determine if it is a palindrome — a word that reads the same forwards and backwards.

9. Write a query to find users currently “Excited” and never “Bored” with a campaign.

Given a table of users’ impressions of ad campaigns, write a query to find all users that are currently “Excited” and have never been “Bored” with a campaign.

10. Create 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 value and next keys. If the linked list is empty, you’ll receive None.

11. Would you think there was anything fishy about the results of an A/B test with 20 variants?

Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with the results?

12. What is the downside of only using the R-Squared ((R^2)) value to determine a relationship between two variables?

You are analyzing how well a model fits the data and want to determine a relationship between two variables. What are the limitations of relying solely on the R-Squared value?

13. Is a coin that comes up tails 8 times and heads twice in 10 flips fair?

You flip a coin 10 times, resulting in 8 tails and 2 heads. Is this coin fair?

14. How would you explain what a p-value is to someone who is not technical?

Explain the concept of a p-value in simple terms to someone without a technical background.

15. What’s the probability that (2X > Y) given two independent standard normal random variables (X) and (Y)?

Given two independent standard normal random variables (X) and (Y), calculate the probability that (2X > Y).

16. What is the difference between XGBoost and random forest algorithms, and when would you use one over the other?

Explain the key differences between XGBoost and random forest algorithms. Provide an example scenario where one algorithm would be more suitable than the other.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Johnson & Johnson data scientist interview include:

• Be Thorough with Past Projects: Be ready to dive deep into the technical and managerial aspects of your previous data science projects, discussing from problem statement to solution implementation.
• Prepare for Behavioral Questions: Brush up on the STAR (Situation, Task, Action, Result) method for behavioral questions, particularly focusing on multitasking and meeting deadlines.
• Know Your Deep Learning Models: If the role involves deep learning, familiarize yourself with image segmentation, data handling, and model advantages, especially related to healthcare data.

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

What qualifications are required for a Principal Data Scientist role?

For a Principal Data Scientist role, a Ph.D. with 2 years of experience, an M.S. with 5 years, or a B.S. with 7 years of relevant experience is required. The degree should be in fields such as Computer Science, Statistics, Machine Learning, or related areas. You should have a strong working knowledge of machine learning platforms, Python or R, SQL, and experience with AI/ML techniques. Proficiency in delivering end-to-end machine learning projects and excellent communication skills are also essential.

What makes Johnson & Johnson an exciting place to work for data scientists?

Johnson & Johnson is at the forefront of healthcare innovation, focusing on preventing, treating, and curing complex diseases. As a data scientist, you will be part of a dynamic team that leverages cutting-edge AI, data science, and advanced analytics to drive impactful solutions. The opportunity to work on groundbreaking projects that optimize patient outcomes and commercial strategies makes J&J a particularly exciting place to work.

The interview process for a Data Scientist position at Johnson & Johnson is a structured yet professional journey that includes multiple rounds—beginning with recruiter and managerial screenings, followed by technical evaluations, and concluding with discussions involving HR and senior leaders. They emphasize practical applications such as developing machine learning models and handling healthcare data.

If you want more insights about the company, check out our main Johnson & Johnson Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Johnson & Johnson’s interview process for different positions.

Good luck with your interview!