Novartis Data Scientist Interview Questions + Guide in 2025

Novartis is a global healthcare company that focuses on innovating drug discovery with artificial intelligence to develop transformative medicines for patients worldwide.

In the role of a Data Scientist at Novartis, you will be responsible for designing, developing, and implementing advanced AI models that enhance drug discovery and disease understanding. Your key responsibilities will include applying deep learning techniques to various data types, including biological sequences and text, while leveraging your extensive knowledge of large language models and AI methodologies. You will engage in hands-on experimentation, utilizing rigorous scientific methodologies to generate publishable results, and work collaboratively with interdisciplinary teams to drive impactful projects. A strong background in AI model development, proficiency in Python programming, and familiarity with statistical analysis are essential to excel in this role. Furthermore, your alignment with Novartis' values of integrity, curiosity, and collaboration will contribute to your success in a fast-paced, innovative environment.

This guide aims to equip you with the insights and understanding necessary to effectively prepare for your interview, ensuring that you can confidently showcase your skills and fit for the Data Scientist role at Novartis.

The interview process for a Data Scientist role at Novartis is structured to assess both technical expertise and cultural fit within the organization. It typically consists of multiple rounds, each designed to evaluate different aspects of your qualifications and experiences.

1. Initial Screening

The process begins with an initial screening, which is often conducted by a recruiter. This round typically lasts around 30 minutes and focuses on understanding your background, motivations for applying to Novartis, and your overall fit for the company culture. Expect to discuss your resume in detail, highlighting relevant experiences and projects that align with the role.

2. Technical and Behavioral Interviews

Following the initial screening, candidates usually participate in one or two rounds of interviews that combine both technical and behavioral assessments. These interviews may involve discussions about your past projects, particularly those related to machine learning, data visualization, and software best practices. You may be asked to explain a technical project from start to finish, showcasing your problem-solving approach and technical skills. Behavioral questions will likely follow the STAR (Situation, Task, Action, Result) format, allowing you to demonstrate your leadership skills and how you handle challenges.

3. Presentation and Research Discussion

In some cases, candidates are asked to present a research project or a significant technical endeavor they have undertaken. This presentation is followed by a Q&A session where interviewers will probe deeper into your research problem, methodologies used, and outcomes achieved. This step is crucial for assessing your ability to communicate complex ideas clearly and effectively.

4. Final Interviews with Leadership

The final stage of the interview process often involves interviews with higher-level management, such as directors or senior leaders within the organization. These interviews may focus on your strategic thinking, vision for the role, and how you can contribute to Novartis's mission of innovating drug discovery through AI. Expect a mix of technical questions and discussions about your long-term career goals and alignment with Novartis's values.

As you prepare for your interviews, consider the types of questions that may arise in these rounds, particularly those that assess your technical knowledge and problem-solving abilities.

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

Understand the Role and Its Impact

Before your interview, take the time to deeply understand how the Data Scientist role at Novartis contributes to the broader mission of innovating drug discovery through AI. Familiarize yourself with the specific projects and technologies the AI & Computational Science (AICS) team is working on, particularly in generative AI and deep learning. This knowledge will allow you to articulate how your skills and experiences align with the team's goals and demonstrate your genuine interest in the role.

Prepare for Technical and Behavioral Questions

Expect a mix of technical and behavioral questions during your interviews. Be ready to discuss your past projects in detail, particularly those involving machine learning, data visualization, and AI model development. Use the STAR (Situation, Task, Action, Result) method to structure your responses to behavioral questions, showcasing your problem-solving skills and leadership experiences. Highlight specific challenges you faced in your projects and how you overcame them, as this will resonate well with the interviewers.

Showcase Your Research and Technical Expertise

Given the emphasis on research and technical skills in the role, prepare to present your previous research projects clearly and concisely. Be ready to explain the methodologies you used, the results you achieved, and how they relate to the work at Novartis. If you have experience with deep learning models, particularly in the context of biological data, be prepared to discuss this in detail, as it will be highly relevant to the position.

Emphasize Collaboration and Teamwork

Novartis values collaboration and an unbossed culture. Be prepared to discuss how you have worked effectively in teams, particularly in interdisciplinary settings. Share examples of how you have contributed to team success and how you have navigated challenges in group dynamics. This will demonstrate your ability to thrive in Novartis's collaborative environment.

Ask Insightful Questions

At the end of your interviews, you will likely have the opportunity to ask questions. Use this time to inquire about the team dynamics, ongoing projects, and how success is measured within the AICS team. Asking thoughtful questions not only shows your interest in the role but also helps you assess if the company culture aligns with your values and career goals.

Reflect Novartis's Values

Throughout your interview, reflect Novartis's commitment to diversity, inclusion, and innovation. Share your perspectives on how diverse teams can drive better outcomes in drug discovery and AI. This alignment with the company's values will resonate with your interviewers and reinforce your fit for the organization.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Scientist role at Novartis. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Novartis. The interview process will likely assess both your technical expertise in data science and your ability to communicate effectively about your past experiences and projects. Be prepared to discuss your knowledge of machine learning, data visualization, and your approach to problem-solving in a collaborative environment.

Machine Learning

1. Can you explain a machine learning project you have worked on from start to finish?

This question aims to assess your practical experience with machine learning projects and your ability to articulate your process clearly.

How to Answer

Detail the problem you were trying to solve, the data you used, the models you implemented, and the results you achieved. Highlight any challenges you faced and how you overcame them.

Example

“I worked on a project to predict patient outcomes using electronic health records. I started by cleaning and preprocessing the data, then I implemented several models, including Random Forest and Gradient Boosting. After evaluating the models, I found that the Gradient Boosting model performed best, achieving an accuracy of 85%. I also collaborated with clinicians to ensure the model's insights were actionable.”

2. What techniques do you use to evaluate the performance of your machine learning models?

This question tests your understanding of model evaluation metrics and your ability to apply them effectively.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when you would use each one based on the context of the problem.

Example

“I typically use accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall to ensure that the model is not biased towards the majority class. For binary classification tasks, I also look at the ROC-AUC score to evaluate the trade-off between true positive and false positive rates.”

3. How do you handle missing data in your datasets?

This question evaluates your data preprocessing skills and your understanding of data integrity.

How to Answer

Explain the methods you use to handle missing data, such as imputation, removal, or using algorithms that support missing values.

Example

“I usually start by analyzing the extent and pattern of missing data. If the missing data is minimal, I might use mean or median imputation. For larger gaps, I consider using predictive models to estimate missing values or, if appropriate, I may remove those records if they do not significantly impact the dataset.”

4. Describe your experience with deep learning frameworks. Which ones have you used?

This question assesses your familiarity with deep learning technologies and your ability to apply them in practice.

How to Answer

Mention specific frameworks you have used, such as TensorFlow or PyTorch, and describe a project where you applied deep learning techniques.

Example

“I have extensive experience with TensorFlow and Keras. In a recent project, I developed a convolutional neural network to classify medical images. I utilized transfer learning with a pre-trained model to improve accuracy and reduce training time, achieving a classification accuracy of over 90%.”

5. What is your approach to feature selection in machine learning?

This question evaluates your understanding of the importance of feature selection in model performance.

How to Answer

Discuss techniques you use for feature selection, such as recursive feature elimination, LASSO regression, or using domain knowledge to identify relevant features.

Example

“I often start with domain knowledge to select initial features, then I use techniques like recursive feature elimination to iteratively remove less important features. I also evaluate feature importance scores from tree-based models to ensure that I retain the most impactful features for my final model.”

Data Visualization

1. How do you approach data visualization in your projects?

This question assesses your ability to communicate data insights effectively through visualization.

How to Answer

Explain your process for selecting the right visualization tools and techniques based on the data and the audience.

Example

“I believe that effective data visualization is crucial for communicating insights. I typically start by identifying the key messages I want to convey and then choose the appropriate visualization type, whether it’s a bar chart, scatter plot, or heatmap. I often use tools like Tableau and Matplotlib to create interactive and informative visualizations.”

2. Can you give an example of a time when your data visualization influenced a decision?

This question evaluates your ability to create impactful visualizations that drive action.

How to Answer

Share a specific instance where your visualization led to a significant decision or change in strategy.

Example

“In a project analyzing patient readmission rates, I created a dashboard that highlighted trends and correlations between various factors. This visualization prompted the clinical team to implement targeted interventions for high-risk patients, resulting in a 15% reduction in readmissions over the next quarter.”

3. What tools do you prefer for data visualization and why?

This question assesses your familiarity with various data visualization tools and your rationale for choosing them.

How to Answer

Discuss the tools you are proficient in and the specific features that make them effective for your work.

Example

“I primarily use Tableau for its user-friendly interface and ability to create interactive dashboards. For more customized visualizations, I prefer Matplotlib and Seaborn in Python, as they offer greater flexibility and control over the aesthetics of the plots.”

4. How do you ensure that your visualizations are accessible to all stakeholders?

This question evaluates your understanding of accessibility in data presentation.

How to Answer

Discuss strategies you use to make visualizations understandable and accessible to a diverse audience.

Example

“I ensure that my visualizations are accessible by using clear labels, color-blind friendly palettes, and providing context through annotations. I also consider the audience’s background and tailor the complexity of the visualizations accordingly, ensuring that everyone can grasp the insights being presented.”

5. Describe a challenging data visualization problem you faced and how you solved it.

This question assesses your problem-solving skills in the context of data visualization.

How to Answer

Share a specific challenge you encountered and the steps you took to overcome it.

Example

“I once faced a challenge when visualizing a large dataset with multiple dimensions. The initial visualizations were cluttered and hard to interpret. To solve this, I broke down the data into smaller subsets and created a series of linked visualizations that allowed users to drill down into specific areas of interest, making the insights much clearer.”