Johnson & Johnson Data Scientist Interview Questions + Guide in 2025

Johnson & Johnson is a global leader in healthcare innovation, dedicated to developing treatments that improve the health of people worldwide across various therapeutic areas.

As a Data Scientist within Johnson & Johnson's Innovative Medicine R&D Data Science and Digital Health team, you will play a pivotal role in leveraging real-world data (RWD) to generate insights that drive clinical development and improve patient outcomes. Your responsibilities will include developing and implementing machine learning models for disease identification, patient stratification, and predictive analytics, while collaborating closely with multi-disciplinary teams to deliver scalable solutions across the healthcare spectrum. A strong foundation in quantitative fields, practical experience in machine learning, and the ability to communicate complex methods to diverse audiences are essential for success in this role. Additionally, familiarity with healthcare data sources such as electronic health records and clinical development data will enhance your contributions to the team’s innovative projects.

This guide aims to equip you with specialized knowledge and insights, enabling you to navigate the interview process with confidence and demonstrate your alignment with Johnson & Johnson's mission and values.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Johnson & Johnson. The interview process will likely assess your technical expertise, problem-solving abilities, and your experience in applying data science methodologies to real-world healthcare challenges. Be prepared to discuss your past projects in detail, as well as your approach to machine learning and statistical analysis.

Machine Learning

1. Describe a machine learning project you have worked on. What was your role, and what were the outcomes?

This question aims to gauge your hands-on experience with machine learning projects and your ability to articulate your contributions.

How to Answer

Focus on the specific project, your responsibilities, the methodologies you employed, and the impact of the project on the organization or stakeholders.

Example

“I led a project that involved developing a predictive model for patient readmission rates using electronic health records. My role included data preprocessing, feature selection, and model evaluation. The model improved our readmission prediction accuracy by 20%, allowing the healthcare team to implement targeted interventions.”

2. How would you approach building a model for patient stratification?

This question assesses your understanding of patient stratification and your ability to apply machine learning techniques in a healthcare context.

How to Answer

Discuss the data sources you would use, the features you would consider, and the algorithms you might apply. Emphasize the importance of interpretability in healthcare models.

Example

“I would start by gathering data from electronic health records and insurance claims. Key features might include demographics, medical history, and treatment plans. I would consider using clustering algorithms like K-means for initial stratification, followed by supervised learning models to refine the groups based on outcomes.”

3. Can you explain the difference between supervised and unsupervised learning? Provide examples relevant to healthcare.

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples that relate to healthcare applications, demonstrating your ability to connect theory with practice.

Example

“Supervised learning involves training a model on labeled data, such as predicting disease outcomes based on patient characteristics. An example is using logistic regression to predict diabetes risk. Unsupervised learning, on the other hand, deals with unlabeled data, like clustering patients based on similar health patterns without predefined categories.”

4. What deep learning models would you consider for image classification tasks in healthcare?

This question evaluates your knowledge of deep learning and its applications in medical imaging.

How to Answer

Mention specific models and their advantages, as well as any relevant experience you have with them.

Example

“I would consider using Convolutional Neural Networks (CNNs) for image classification tasks, such as detecting tumors in X-ray images. CNNs are effective due to their ability to capture spatial hierarchies in images. I have previously implemented a CNN that achieved a 95% accuracy rate in classifying MRI scans.”

5. How do you handle overfitting in your models?

This question assesses your understanding of model evaluation and improvement techniques.

How to Answer

Discuss various strategies you use to prevent overfitting, such as cross-validation, regularization, and using simpler models.

Example

“To handle overfitting, I typically use techniques like cross-validation to ensure my model generalizes well to unseen data. I also apply regularization methods like L1 or L2 to penalize overly complex models. In a recent project, these strategies helped reduce overfitting and improved model performance on validation data.”

Statistics & Probability

1. Explain the concept of p-value and its significance in hypothesis testing.

This question tests your understanding of statistical concepts that are crucial in data analysis.

How to Answer

Define p-value and explain its role in determining statistical significance.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value (typically < 0.05) suggests that we can reject the null hypothesis, indicating that our findings are statistically significant.”

2. How would you assess the effectiveness of a new treatment using statistical methods?

This question evaluates your ability to apply statistical analysis in a healthcare context.

How to Answer

Outline the steps you would take, including study design, data collection, and analysis methods.

Example

“I would design a randomized controlled trial to assess the treatment's effectiveness. After collecting data, I would use statistical tests like t-tests or ANOVA to compare outcomes between treatment and control groups, ensuring to account for confounding variables.”

3. What is the Central Limit Theorem, and why is it important?

This question tests your foundational knowledge of statistics.

How to Answer

Explain the theorem and its implications for statistical inference.

Example

“The Central Limit Theorem states that the distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial for making inferences about population parameters based on sample statistics.”

4. Can you describe a time when you used statistical analysis to solve a problem?

This question assesses your practical experience with statistical methods.

How to Answer

Provide a specific example, detailing the problem, the analysis you performed, and the outcome.

Example

“In a project analyzing patient outcomes, I used regression analysis to identify factors influencing recovery times. By isolating variables like age and treatment type, I was able to provide actionable insights that led to improved patient care protocols.”

5. How do you ensure the validity and reliability of your statistical analyses?

This question evaluates your understanding of best practices in statistical analysis.

How to Answer

Discuss the methods you use to validate your analyses, such as using appropriate sample sizes and conducting sensitivity analyses.

Example

“I ensure validity by using adequate sample sizes and random sampling methods. I also perform sensitivity analyses to check how results change with different assumptions, which helps confirm the robustness of my findings.”

Here are some tips to help you excel in your interview.

Embrace the STAR Method

When responding to behavioral questions, utilize the STAR method (Situation, Task, Action, Result) to structure your answers. This approach not only helps you articulate your experiences clearly but also demonstrates your problem-solving skills and ability to reflect on past challenges. For instance, if asked about managing multiple deadlines, outline a specific situation, the tasks involved, the actions you took, and the results achieved. This structured response will resonate well with interviewers who value clarity and depth in your answers.

Prepare for Technical Depth

Given the technical nature of the Data Scientist role, be prepared to discuss your past projects in detail, especially those involving machine learning and deep learning models. Familiarize yourself with the specific methodologies you used, the challenges you faced, and how you overcame them. You may be asked to explain your approach to handling complex datasets or to describe the advantages of different models in healthcare applications. This level of detail will showcase your expertise and readiness for the role.

Know Your Projects Inside and Out

Expect to discuss your resume and past projects extensively. Be ready to dive deep into the specifics of your work, including the data sources you utilized, the analytical techniques you applied, and the outcomes of your projects. This is particularly important as interviewers will likely want to assess your hands-on experience and how it aligns with the responsibilities of the role. Highlight any experience you have with real-world data (RWD) and how it has informed your decision-making in previous roles.

Communicate with Clarity

Johnson & Johnson values clear communication, especially when discussing complex technical concepts. Practice explaining your work in a way that is accessible to non-technical stakeholders. This skill is crucial as you will need to present your findings and methodologies to diverse audiences, including senior leadership. Tailoring your communication style to your audience will demonstrate your ability to bridge the gap between technical and non-technical teams.

Be Patient and Persistent

The interview process at Johnson & Johnson can involve long gaps between rounds and may require follow-ups for updates. Stay patient and proactive in your communication with HR. If you experience delays, don’t hesitate to reach out for updates, as this shows your continued interest in the position. However, maintain professionalism in your correspondence to leave a positive impression.

Showcase Your Collaborative Spirit

Collaboration is key in a multidisciplinary environment like Johnson & Johnson. Be prepared to discuss how you have worked with cross-functional teams in the past. Highlight your ability to influence and engage with various stakeholders, as this will be essential in executing projects and driving results in the role. Sharing examples of successful collaborations will illustrate your fit within the company culture.

Understand the Company Culture

Johnson & Johnson prides itself on a diverse and inclusive culture. Familiarize yourself with their values and mission, particularly their commitment to improving health outcomes globally. Reflect on how your personal values align with the company’s mission and be prepared to discuss this during your interview. This alignment can significantly enhance your candidacy.

By following these tips, you will be well-prepared to navigate the interview process and demonstrate your qualifications for the Data Scientist role at Johnson & Johnson. Good luck!

The interview process for a Data Scientist role at Johnson & Johnson is structured and thorough, designed to assess both technical and interpersonal skills. Here’s a breakdown of the typical steps involved:

1. Application and Initial Screening

The process begins with submitting an application through Johnson & Johnson's online platform. After your application is reviewed, you may receive an invitation for an initial phone screening. This call typically lasts around 30-45 minutes and is conducted by a recruiter. During this conversation, the recruiter will discuss your resume, relevant experiences, and the role itself, while also gauging your fit within the company culture.

2. Technical Assessment

Following the initial screening, candidates may be invited to participate in a technical assessment. This could take the form of a video interview where you will be asked to solve problems related to data science, such as statistical analysis, machine learning models, or data manipulation tasks. You may also be required to discuss specific projects from your past experience, demonstrating your technical knowledge and problem-solving abilities.

3. Behavioral Interviews

Candidates typically undergo one or more behavioral interviews, which focus on assessing soft skills and cultural fit. Interviewers will ask questions that require you to provide examples of past experiences, often using the STAR (Situation, Task, Action, Result) format. This is an opportunity to showcase your teamwork, leadership, and communication skills, as well as how you handle challenges and deadlines.

4. Presentation Round

In some cases, candidates may be asked to prepare a presentation on a relevant topic or a past project. This presentation allows you to demonstrate your ability to communicate complex ideas clearly and effectively to a diverse audience. Interviewers will evaluate not only the content of your presentation but also your presentation skills and how well you engage with the audience.

5. Final Interviews with Leadership

If you successfully pass the previous rounds, you may be invited for final interviews with senior leadership or cross-functional teams. This stage is crucial as it assesses your strategic thinking and ability to align with the company’s goals. You may be asked to discuss your vision for the role and how you can contribute to the team and the organization as a whole.

6. Follow-Up and Offer

After the final interviews, the hiring team will review all candidates and make a decision. You can expect a follow-up regarding the outcome of your interviews, although some candidates have reported delays in communication. If selected, you will receive a formal job offer, which will include details about compensation and benefits.

As you prepare for your interview, consider the types of questions that may arise in each of these stages.

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 consider 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 backward.

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. 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?

12. 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?

13. 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.

14. 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).

15. 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!