Novartis ML Engineer Interview Guide

Getting ready for an ML Engineer interview at Novartis? The Novartis ML Engineer interview process typically spans a diverse set of question topics and evaluates skills in areas like machine learning algorithms, system design, data engineering, and communicating complex technical insights to non-technical audiences. Interview preparation is especially important for this role at Novartis, as candidates are expected to demonstrate not only technical expertise but also a strong ability to translate data-driven solutions into practical impact within healthcare and life sciences.

In preparing for the interview, you should:

• Understand the core skills necessary for ML Engineer positions at Novartis.
• Gain insights into Novartis’s ML Engineer interview structure and process.
• Practice real Novartis ML Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the Novartis ML Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

Novartis is a leading global pharmaceutical company focused on discovering, developing, and commercializing innovative medicines to improve patient outcomes and address unmet medical needs. Operating in more than 150 countries, Novartis combines cutting-edge science with advanced technology to drive breakthroughs in healthcare. The company is committed to leveraging data and digital solutions, making the role of an ML Engineer pivotal in enhancing drug development, optimizing clinical trials, and accelerating research through machine learning and artificial intelligence.

As an ML Engineer at Novartis, you will develop, implement, and optimize machine learning models to support drug discovery, clinical research, and operational efficiency within the company’s healthcare initiatives. You will collaborate with data scientists, bioinformaticians, and software engineers to process large datasets, build predictive algorithms, and deploy scalable solutions in production environments. Key responsibilities include data preprocessing, model training and validation, and ensuring compliance with regulatory standards for data privacy and security. This role is integral to advancing Novartis’s mission of improving patient outcomes by leveraging cutting-edge AI and machine learning technologies in pharmaceutical research and development.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your application materials, focusing on your experience in machine learning engineering, proficiency with model development, and your ability to communicate technical concepts clearly. The recruiting team and technical leads look for evidence of hands-on project work, familiarity with ML system design, and contributions to data-driven solutions in healthcare or related fields. To prepare, ensure your resume highlights impactful ML projects, your role in data cleaning and preparation, and your ability to build scalable solutions.

2.2 Stage 2: Recruiter Screen

A screening call with an HR or talent acquisition specialist is typically conducted to assess your motivation for joining Novartis, alignment with company values, and general understanding of the ML engineering role. Expect questions around your career trajectory, strengths and weaknesses, and your interest in the healthcare domain. Preparation should include concise narratives about your background, reasons for applying, and how your skills match Novartis’ mission.

2.3 Stage 3: Technical/Case/Skills Round

This stage usually consists of one or more technical interviews and a take-home assignment. You may be asked to solve ML engineering problems, demonstrate your coding skills (such as implementing logistic regression from scratch or building scalable ETL pipelines), and discuss your approach to model evaluation, bias-variance tradeoff, and handling imbalanced data. The take-home assignment will often require you to analyze a dataset, design an ML solution, or present insights clearly and effectively. Preparation should focus on practical ML problem-solving, familiarity with relevant algorithms, and the ability to communicate your methodology and findings.

2.4 Stage 4: Behavioral Interview

A behavioral interview is conducted to evaluate your collaboration skills, adaptability, and ability to communicate complex technical insights to non-expert audiences. You’ll be expected to share examples of overcoming hurdles in data projects, exceeding expectations, and making data accessible for diverse stakeholders. Prepare by reflecting on your experiences working in cross-functional teams, presenting data-driven recommendations, and navigating project challenges.

2.5 Stage 5: Final/Onsite Round

The final round typically involves a self-introduction and a presentation of previous projects to a panel that may include ML team leads, product managers, and senior stakeholders. You’ll be expected to showcase your end-to-end ownership of ML initiatives, explain your technical decision-making, and answer probing questions about your approach and impact. Preparation should center on structuring your presentation for clarity, emphasizing your contributions, and tailoring your communication for both technical and non-technical audiences.

2.6 Stage 6: Offer & Negotiation

Once interviews are complete, successful candidates will engage with the HR team for offer discussions, including compensation, benefits, and potential start dates. This stage may also involve clarifying your role’s scope and expectations within the ML engineering team. Prepare by researching industry standards, prioritizing your preferences, and being ready to articulate your value proposition.

2.7 Average Timeline

The Novartis ML Engineer interview process typically spans 3-5 weeks from initial application to final offer, with most candidates experiencing a week between each stage. Fast-track applicants with highly relevant experience or internal referrals may progress in as little as 2-3 weeks, while those requiring additional technical assessment or scheduling flexibility may take longer. The take-home assignment usually comes with a 3-5 day deadline, and the final presentation is scheduled based on panel availability.

Next, let’s explore the types of interview questions you can expect at each stage.

3.1. Machine Learning Fundamentals

Expect questions that assess your understanding of core ML concepts, model selection, and the ability to explain technical topics simply. Demonstrating both theoretical knowledge and practical application is key.

3.1.1 Explain how you would justify using a neural network for a particular problem, including its advantages and potential drawbacks compared to other algorithms
To answer, discuss the characteristics of the problem, data size, and complexity that make neural networks suitable. Highlight scenarios where simpler models may suffice or outperform deep learning.

3.1.2 Describe what is unique about the Adam optimization algorithm and when you would choose it over other optimizers
Explain Adam’s adaptive learning rate and moment estimation, and compare its convergence properties to SGD and RMSProp. Provide context for choosing Adam in large, sparse, or noisy datasets.

3.1.3 Explain the bias vs. variance tradeoff and how you would address it when developing a machine learning model
Define both bias and variance, describe their impact on model performance, and outline strategies for balancing them, such as regularization or cross-validation.

3.1.4 When should you consider using Support Vector Machines rather than deep learning models?
Discuss dataset size, feature dimensionality, interpretability, and computational resources as factors in choosing SVMs over deep learning.

3.1.5 Why would one algorithm generate different success rates with the same dataset?
Address causes like random initialization, data splits, hyperparameter choices, and stochastic processes in training.

3.2. Deep Learning & Neural Networks

These questions focus on your understanding of neural network architectures, optimization, and their practical deployment. Be ready to demonstrate both conceptual clarity and the ability to communicate complex ideas.

3.2.1 Explain neural networks to someone without a technical background, such as a child
Use analogies and simple language to break down the concept, ensuring your explanation is accessible and memorable.

3.2.2 Describe the process of backpropagation and its role in training deep neural networks
Summarize how gradients are computed and propagated to update weights, and why this is essential for learning.

3.2.3 How does scaling a neural network with more layers impact its performance and training?
Discuss the effects on expressiveness, overfitting, vanishing/exploding gradients, and computational demands.

3.2.4 Describe the Inception architecture and its advantages in deep learning models
Explain the concept of parallel convolutional layers and how they improve model performance and efficiency.

3.3. Model Design & Evaluation

Here, you’ll be tested on your ability to design, implement, and evaluate machine learning systems for real-world problems. Emphasize your structured approach and awareness of practical challenges.

3.3.1 Identify the requirements for building a machine learning model that predicts subway transit patterns
Outline the data sources, features, target variables, and evaluation criteria you’d use for such a predictive system.

3.3.2 How would you design a machine learning model for evaluating a patient’s health risk?
Describe your process for feature selection, model choice, validation, and how you’d ensure fairness and accuracy.

3.3.3 Explain how you would address imbalanced data when developing a machine learning solution
Discuss sampling strategies, algorithmic adjustments, and evaluation metrics suitable for imbalanced datasets.

3.3.4 Describe how you would evaluate the effectiveness of a decision tree model
Cover metrics, validation techniques, and how you’d interpret and improve the model’s performance.

3.4. Data Engineering & System Design

These questions assess your ability to build scalable, reliable ML systems and data pipelines. Focus on demonstrating your understanding of system architecture, data flow, and optimization for production environments.

3.4.1 Design a scalable ETL pipeline for ingesting heterogeneous data from multiple external partners
Detail your approach to data ingestion, transformation, validation, and storage, considering scalability and reliability.

3.4.2 How would you design a feature store for credit risk ML models and integrate it with a cloud platform?
Explain the components of a feature store, data versioning, access patterns, and integration with model training and serving pipelines.

3.4.3 Describe the challenges and solutions for modifying a billion rows in a production database
Discuss strategies for minimizing downtime, ensuring data integrity, and monitoring the update process.

3.5. Communication & Impact

Effective ML engineers must communicate insights and recommendations clearly, especially to non-technical stakeholders. These questions test your ability to translate technical results into business value.

3.5.1 How would you present complex data insights with clarity and adaptability tailored to a specific audience?
Describe your approach to storytelling, visualization, and adjusting your message for technical and non-technical listeners.

3.5.2 How do you make data-driven insights actionable for those without technical expertise?
Explain how you simplify concepts, use analogies, and focus on practical implications.

3.5.3 How do you demystify data for non-technical users through visualization and clear communication?
Highlight techniques for building intuitive dashboards and guiding stakeholders to actionable decisions.

3.6. Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision that significantly impacted a project or business outcome.
How to Answer: Choose a specific example, outline your analytical approach, and emphasize the measurable impact of your recommendation.
Example: I analyzed customer churn data, identified key drivers, and recommended a targeted retention campaign that reduced churn by 15%.

3.6.2 Describe a challenging data project and how you handled obstacles during its execution.
How to Answer: Focus on the problem, your systematic approach to overcoming it, and what you learned.
Example: In a project with highly imbalanced classes, I implemented SMOTE and adjusted evaluation metrics to ensure reliable results.

3.6.3 How do you handle unclear requirements or ambiguity in project goals?
How to Answer: Explain your process for clarifying objectives, engaging stakeholders, and iterating on solutions.
Example: I organized discovery sessions with stakeholders, drafted initial hypotheses, and refined requirements through feedback loops.

3.6.4 Tell me about a time you had to communicate complex technical findings to a non-technical audience.
How to Answer: Describe your communication strategy, use of visuals, and how you ensured understanding.
Example: I used simple analogies and interactive dashboards to explain model predictions to senior management.

3.6.5 Share a story where you used data prototypes or wireframes to align stakeholders with different visions of the final deliverable.
How to Answer: Highlight your use of rapid prototyping and collaborative feedback to converge on a solution.
Example: I built a dashboard mockup in Tableau, gathered feedback from marketing and product teams, and iterated quickly to meet both needs.

3.6.6 How have you balanced speed versus rigor when leadership needed a “directional” answer by tomorrow?
How to Answer: Describe your triage process, prioritization of critical data cleaning, and how you communicated uncertainty.
Example: I focused on high-impact issues, presented results with confidence intervals, and documented follow-up steps for deeper analysis.

3.6.7 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
How to Answer: Explain your automation approach, tools used, and the impact on team efficiency and data reliability.
Example: I developed scheduled SQL scripts and alerting in Airflow, reducing manual QA time by 60%.

3.6.8 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
How to Answer: Discuss your approach to building consensus, presenting evidence, and addressing concerns.
Example: I shared pilot results and facilitated cross-team workshops, leading to successful adoption of a new forecasting model.

3.6.9 Describe a time you delivered critical insights even though a significant portion of the dataset had missing values.
How to Answer: Outline your missing data treatment, transparency in reporting, and how you enabled business decisions.
Example: I used multiple imputation for missing values, clearly communicated limitations, and highlighted actionable trends.

4.1 Company-specific tips:

Familiarize yourself with Novartis’s mission to improve patient outcomes through innovation in medicine and technology. Understand how machine learning is transforming drug discovery, clinical trial optimization, and patient risk prediction within the pharmaceutical industry. Review recent Novartis initiatives, such as AI-driven research collaborations, digital therapeutics, and data privacy efforts in healthcare. Be prepared to discuss how your expertise in ML can contribute to the company’s goals of accelerating research and delivering value to patients worldwide.

Demonstrate your awareness of regulatory requirements and ethical considerations in healthcare data, such as HIPAA and GDPR compliance. Novartis places a strong emphasis on data privacy, so be ready to explain how you ensure the security and integrity of sensitive patient information when building ML solutions. Connect your experience to the real-world impact of AI in medicine—highlight projects where your work led to improved outcomes, operational efficiency, or innovation in life sciences.

Showcase your ability to communicate technical concepts to cross-functional teams, including clinicians, researchers, and business stakeholders. Novartis values ML engineers who can bridge the gap between technical rigor and practical application. Practice explaining complex models and results in accessible terms, and prepare examples of how you’ve made data-driven insights actionable for non-technical audiences.

4.2 Role-specific tips:

4.2.1 Master the fundamentals of ML algorithms and their application to healthcare problems.
Review classic and advanced ML algorithms such as logistic regression, support vector machines, decision trees, and neural networks. Be ready to justify your choice of models for specific healthcare use cases, considering factors like interpretability, scalability, and data characteristics. Practice articulating the advantages and limitations of each algorithm, especially in the context of clinical data and medical research.

4.2.2 Be adept at data preprocessing and handling real-world healthcare datasets.
Expect to discuss your approach to cleaning, normalizing, and transforming messy or incomplete data. Highlight your experience working with imbalanced datasets, missing values, and heterogeneous data sources—common challenges in pharmaceutical research. Prepare to explain your strategies for feature engineering, data augmentation, and ensuring the reliability of your input data.

4.2.3 Demonstrate practical knowledge of model evaluation and validation.
Novartis will assess your ability to select appropriate metrics (such as ROC-AUC, precision-recall, F1 score) and validation techniques (cross-validation, bootstrapping) for healthcare ML models. Be prepared to discuss the bias-variance tradeoff, approaches for handling overfitting, and ways to evaluate models when data is scarce or imbalanced. Share examples of how you’ve measured and improved model performance in past projects.

4.2.4 Show expertise in scalable ML system design and data engineering.
Highlight your experience building and optimizing ETL pipelines, designing feature stores, and integrating ML solutions with cloud platforms. Novartis values engineers who can deploy robust, production-ready models that handle large-scale data from diverse sources. Be ready to describe your approach to system architecture, data flow, and maintaining reliability in high-volume environments.

4.2.5 Prepare to communicate technical insights with clarity and impact.
You’ll be expected to present your work to panels including ML leads, product managers, and senior stakeholders. Practice structuring your presentations for clarity, emphasizing your decision-making process, and tailoring your message for both technical and non-technical audiences. Use storytelling and visualization to make your findings memorable and actionable.

4.2.6 Reflect on behavioral competencies such as collaboration, adaptability, and stakeholder influence.
Novartis seeks ML engineers who thrive in cross-functional teams and can drive consensus without formal authority. Prepare stories that demonstrate your teamwork, resilience in the face of project challenges, and ability to influence decision-making through data. Highlight your experience navigating ambiguity, balancing speed and rigor, and automating processes for greater efficiency.

4.2.7 Be ready to discuss your end-to-end ownership of ML projects.
Show that you can take initiative from problem definition through deployment and impact measurement. Explain your methodology for designing solutions, iterating based on feedback, and delivering measurable business value. Emphasize your ability to align diverse stakeholders using prototypes, wireframes, and clear communication.

4.2.8 Stay current on the latest trends in ML for healthcare and pharmaceuticals.
Demonstrate your curiosity and commitment to learning by referencing recent advancements in AI-driven drug discovery, real-world evidence generation, and personalized medicine. Be prepared to discuss how emerging technologies can shape Novartis’s future and how you plan to contribute to that vision as an ML Engineer.

5.1 How hard is the Novartis ML Engineer interview?
The Novartis ML Engineer interview is considered challenging, especially for candidates new to healthcare or pharmaceutical data. Expect rigorous evaluation of your machine learning fundamentals, system design skills, and ability to communicate technical concepts to non-experts. The process emphasizes practical impact—showing how your ML expertise can drive innovation in drug discovery, clinical trials, and patient outcomes. Candidates with strong hands-on experience and a clear understanding of healthcare data complexities will be well-positioned to succeed.

5.2 How many interview rounds does Novartis have for ML Engineer?
Typically, the Novartis ML Engineer interview process consists of five to six rounds: an initial application and resume review, a recruiter screen, one or more technical/case interviews (often including a take-home assignment), a behavioral interview, and a final onsite or virtual panel presentation. Some candidates may encounter an additional technical deep-dive or stakeholder interview, depending on the team.

5.3 Does Novartis ask for take-home assignments for ML Engineer?
Yes, most Novartis ML Engineer candidates receive a take-home assignment as part of the technical evaluation. These assignments usually involve analyzing a real-world dataset, designing a machine learning solution, or presenting actionable insights tailored to healthcare use cases. You’ll be assessed on your problem-solving approach, coding proficiency, and clarity in communicating results.

5.4 What skills are required for the Novartis ML Engineer?
Key skills for Novartis ML Engineers include mastery of machine learning algorithms (both classic and deep learning), data preprocessing, model evaluation, scalable system design, and cloud integration. Strong Python programming, experience with healthcare or biomedical data, and the ability to communicate technical findings to diverse audiences are highly valued. Familiarity with regulatory standards (such as HIPAA and GDPR) and a passion for applying ML to improve patient outcomes will set you apart.

5.5 How long does the Novartis ML Engineer hiring process take?
On average, the Novartis ML Engineer interview process spans 3-5 weeks from initial application to final offer. Each stage typically takes about a week, though scheduling and panel availability can affect the timeline. Fast-track candidates or those with internal referrals may progress more quickly, while additional technical assessments can extend the process.

5.6 What types of questions are asked in the Novartis ML Engineer interview?
Expect a mix of technical, case-based, and behavioral questions. Technical rounds cover machine learning fundamentals, deep learning, system design, and data engineering. You’ll solve practical ML problems, discuss model choices for healthcare scenarios, and demonstrate your approach to handling real-world data challenges. Behavioral interviews focus on collaboration, adaptability, and communication—especially your ability to make complex insights accessible to non-technical stakeholders.

5.7 Does Novartis give feedback after the ML Engineer interview?
Novartis typically provides high-level feedback through recruiters, especially if you progress to later rounds. While detailed technical feedback may be limited, you can expect constructive insights on your strengths and areas for improvement. Candidates are encouraged to request feedback to guide their future interview preparation.

5.8 What is the acceptance rate for Novartis ML Engineer applicants?
The acceptance rate for Novartis ML Engineer applicants is competitive, estimated at around 3-6% for qualified candidates. The company receives a high volume of applications for ML roles, and successful candidates usually demonstrate exceptional technical skills, healthcare domain understanding, and strong communication abilities.

5.9 Does Novartis hire remote ML Engineer positions?
Yes, Novartis offers remote ML Engineer roles, with flexibility depending on the team and project requirements. Some positions may require occasional onsite collaboration or travel for key meetings, but remote work is increasingly supported, especially for global teams working on digital health initiatives.

• Related guides:
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  • ML Engineer interview guide
  • Top machine learning interview tips

Ready to ace your Novartis ML Engineer interview? It’s not just about knowing the technical skills—you need to think like a Novartis ML Engineer, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Novartis and similar companies.

With resources like the Novartis ML Engineer Interview Guide and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition. Whether you’re preparing to justify neural network choices, design scalable ETL pipelines, or communicate complex insights to healthcare stakeholders, these resources will help you master the unique challenges of the Novartis interview process.

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