Novo Nordisk Machine Learning Engineer Interview Questions + Guide in 2025

Novo Nordisk is a global healthcare company with a passion for improving the lives of people with chronic diseases.

As a Machine Learning Engineer at Novo Nordisk, you will play a critical role in leveraging data-driven solutions to enhance drug development and patient care. Your key responsibilities will include designing, implementing, and optimizing machine learning models to analyze vast datasets, collaborating with cross-functional teams to ensure the integration of these models into existing workflows, and continually refining algorithms based on feedback and performance metrics. A strong understanding of statistical modeling, proficiency in programming languages such as Python or R, and experience with machine learning frameworks like TensorFlow or PyTorch are essential for success in this role. Additionally, you should possess excellent problem-solving skills, a collaborative mindset, and the ability to communicate complex technical concepts to non-technical stakeholders.

This guide will help you prepare effectively for your interview by providing insights into the expectations and nuances of the role, along with specific areas to focus on during your preparation.

The interview process for a Machine Learning Engineer at Novo Nordisk is structured and thorough, designed to assess both technical skills and cultural fit within the organization. The process typically unfolds in several stages:

1. Initial Screening

The first step usually involves a phone screening with an HR representative or recruiter. This conversation lasts about 30 to 45 minutes and focuses on your background, motivation for applying, and general fit for the company culture. Expect questions about your previous experiences and how they relate to the role at Novo Nordisk.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview. This may be conducted via video call and can last up to an hour. During this interview, you will be asked to demonstrate your technical expertise in machine learning concepts, algorithms, and tools relevant to the position. You may also be required to solve coding challenges or discuss past projects in detail, including the methodologies and technologies used.

3. Presentation Round

In some cases, candidates are asked to prepare a presentation on a relevant project or research they have conducted. This presentation is usually followed by a Q&A session where interviewers will probe deeper into your work, asking for clarifications on your model choices, results, and the implications of your findings. This round is crucial as it allows interviewers to assess your communication skills and ability to articulate complex concepts clearly.

4. Behavioral Interview

A behavioral interview is often included in the process, where you will be asked competency-based questions. These questions aim to evaluate how you handle various situations, your teamwork and conflict resolution skills, and your alignment with Novo Nordisk's values. Be prepared to discuss specific examples from your past experiences that demonstrate your problem-solving abilities and interpersonal skills.

5. Final HR Round

The final stage typically involves a conversation with HR, focusing on your overall fit within the company and discussing any remaining questions you may have. This round may also include discussions about salary expectations and benefits.

Throughout the process, candidates may also undergo personality and cognitive assessments to further gauge their fit for the role and the company culture.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and past experiences.

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

Understand the Interview Structure

Novo Nordisk typically conducts a multi-stage interview process that may include phone screenings, technical assessments, and multiple rounds with various team members. Familiarize yourself with this structure and prepare accordingly. Expect to discuss your past experiences in detail, as well as to present your work through slides or case studies. This will help you navigate the process smoothly and demonstrate your preparedness.

Prepare for Behavioral Questions

Behavioral questions are a significant part of the interview process at Novo Nordisk. Be ready to discuss your past experiences, particularly how you handle conflicts, work under pressure, and collaborate with others. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples that highlight your problem-solving skills and adaptability.

Showcase Your Technical Expertise

As a Machine Learning Engineer, you will likely face technical questions related to your field. Brush up on relevant algorithms, model evaluation techniques, and programming languages commonly used in machine learning, such as Python or R. Be prepared to discuss your previous projects in detail, including the challenges you faced and how you overcame them. This will demonstrate your technical competence and ability to apply your knowledge in real-world scenarios.

Emphasize Cultural Fit

Novo Nordisk values a collaborative and respectful work environment. During your interview, convey your understanding of the company's mission and how your values align with theirs. Be prepared to discuss how you contribute to a positive team dynamic and how you handle differing opinions. This will help you stand out as a candidate who not only possesses the necessary skills but also fits well within the company culture.

Be Ready for Personality Assessments

Novo Nordisk often incorporates personality tests into their hiring process. These assessments are designed to gauge your fit within the team and the organization. Approach these tests with honesty and self-awareness, as they are an integral part of the evaluation process. Be prepared to discuss the results during your interviews, as interviewers may ask how you relate to the findings and how they impact your work style.

Ask Insightful Questions

At the end of your interviews, you will likely have the opportunity to ask questions. Use this time to demonstrate your interest in the role and the company. Inquire about team dynamics, ongoing projects, or the company's future direction. This not only shows your enthusiasm but also helps you assess whether Novo Nordisk is the right fit for you.

Follow Up Professionally

After your interviews, consider sending a thank-you email to express your appreciation for the opportunity to interview. This is a chance to reiterate your interest in the position and briefly highlight how your skills align with the company's needs. A thoughtful follow-up can leave a positive impression and keep you top of mind as they make their decision.

By following these tips, you can approach your interview with confidence and clarity, increasing your chances of success at Novo Nordisk. Good luck!

Machine Learning Concepts

1. Can you explain the differences between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial for this role. Be prepared to discuss the characteristics and applications of both types of learning.

How to Answer

Clearly define both supervised and unsupervised learning, providing examples of algorithms and scenarios where each is applicable.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as using regression for predicting house prices. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

2. What are some common challenges you face when deploying machine learning models?

This question assesses your practical experience and problem-solving skills in real-world applications.

How to Answer

Discuss specific challenges such as data quality, model drift, or integration with existing systems, and how you have addressed them in past projects.

Example

“One common challenge is model drift, where the model's performance degrades over time due to changes in data patterns. To combat this, I implement regular monitoring and retraining schedules to ensure the model remains accurate and relevant.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics and their importance.

How to Answer

Mention various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, while precision and recall are crucial for imbalanced datasets. For instance, in a medical diagnosis model, I prioritize recall to minimize false negatives.”

4. Describe a machine learning project you worked on and the impact it had.

This question allows you to showcase your experience and the value you bring to the company.

How to Answer

Outline the project scope, your role, the techniques used, and the results achieved, emphasizing the impact on the business.

Example

“I led a project to develop a predictive maintenance model for manufacturing equipment, which reduced downtime by 30%. By analyzing sensor data with time series forecasting, we were able to predict failures before they occurred.”

Statistics and Probability

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

This question assesses your foundational knowledge in statistics, which is essential for data analysis.

How to Answer

Explain the theorem and its implications for sampling distributions and inferential statistics.

Example

“The Central Limit Theorem states that the distribution of sample means 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 data.”

2. How do you handle missing data in a dataset?

This question evaluates your data preprocessing skills, which are vital for effective model training.

How to Answer

Discuss various techniques such as imputation, deletion, or using algorithms that support missing values, and when to apply each.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. If it’s minimal, I might use mean imputation, but for larger gaps, I prefer predictive imputation methods to maintain data integrity.”

3. Can you explain the difference between Type I and Type II errors?

Understanding these concepts is critical for hypothesis testing and decision-making in machine learning.

How to Answer

Define both types of errors and provide examples to illustrate their implications.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, in a medical test, a Type I error could mean falsely diagnosing a disease, while a Type II error could mean missing a diagnosis.”

4. What is overfitting, and how can it be prevented?

This question tests your understanding of model training and validation techniques.

How to Answer

Define overfitting and discuss strategies such as cross-validation, regularization, and pruning.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor generalization. To prevent this, I use techniques like cross-validation to ensure the model performs well on unseen data and apply regularization to penalize overly complex models.”

Behavioral and Situational Questions

1. Describe a time you faced a conflict in a team setting. How did you handle it?

This question assesses your interpersonal skills and ability to work collaboratively.

How to Answer

Provide a specific example, focusing on your approach to resolving the conflict and the outcome.

Example

“In a previous project, there was a disagreement over the choice of algorithms. I facilitated a meeting where each team member presented their rationale. By encouraging open dialogue, we reached a consensus on a hybrid approach that combined the strengths of both algorithms.”

2. How do you prioritize tasks when working on multiple projects?

This question evaluates your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including tools or methods you use to manage your workload effectively.

Example

“I prioritize tasks based on deadlines and project impact. I use project management tools like Trello to visualize my workload and ensure that I allocate time effectively, focusing on high-impact tasks first.”

3. What motivates you to work in the biopharmaceutical industry?

This question gauges your passion for the field and alignment with the company’s mission.

How to Answer

Share your personal motivations and how they connect to the company’s goals and values.

Example

“I am motivated by the opportunity to contribute to advancements in healthcare. Working in the biopharmaceutical industry allows me to apply my machine learning skills to projects that can improve patient outcomes and save lives, which aligns with my personal values.”

4. How do you handle stress and tight deadlines?

This question assesses your coping strategies and resilience under pressure.

How to Answer

Discuss specific techniques you use to manage stress and ensure productivity during high-pressure situations.

Example

“I handle stress by maintaining a structured schedule and breaking tasks into manageable parts. During tight deadlines, I prioritize communication with my team to ensure we’re aligned and can support each other, which helps alleviate pressure.”