The questions below represent the typical themes and scenarios you will encounter during your Novartis interviews. While you should not memorize answers, use these to practice structuring your thoughts, especially focusing on how you articulate your past experiences and design decisions.

Resume & Technical Deep Dive

This category focuses heavily on the specifics of your past work. Interviewers want to verify your hands-on experience with APIs, model building, and engineering best practices.

• Walk me through the architecture of the most impactful project on your resume.
• How did you design the API for [Specific Project]? What frameworks did you use and why?
• Explain a time when you had to optimize a machine learning model for better performance or lower latency.
• Describe your process for ensuring code quality and reliability when deploying AI models.
• What were the biggest technical challenges you faced in [Specific Project], and how did you overcome them?

System Design & Workflow

These questions test your ability to architect scalable solutions and think through the entire lifecycle of an AI project.

• How would you design a scalable workflow to process daily batches of clinical trial data and update a predictive model?
• Walk us through your approach to transitioning a model from a local Jupyter notebook to a production environment.
• If we need to serve a model that requires real-time inference with strict latency constraints, how would you architect the serving layer?
• How do you monitor model drift in production, and what automated workflows would you set up to handle it?
• Present a high-level design for a system that ingests unstructured medical texts and securely exposes an NLP model via an API.

Behavioral & Stakeholder Management

Novartis places high value on collaboration. These questions evaluate your emotional intelligence, adaptability, and communication skills.

• Tell me about a time you had to manage conflicting priorities from different stakeholders.
• Describe a situation where you had to explain a highly technical AI concept to a business leader. How did you ensure they understood?
• Can you share an example of a project that failed or did not meet expectations? What did you learn, and how did you communicate this to the team?
• How do you handle situations where a stakeholder has unrealistic expectations about what machine learning can achieve?
• Tell me about a time you successfully collaborated with a cross-functional team to deliver a complex project.

This visual timeline outlines the typical progression of the Novartis interview process, moving from initial behavioral alignment to deep technical and project-based evaluations. You should use this to pace your preparation, ensuring you are ready to discuss both your technical architecture skills and your stakeholder management experiences by the final rounds. Note that specific stages, like the presentation component, may vary slightly depending on the exact team or location you are interviewing with.

5. Deep Dive into Evaluation Areas

To succeed in the Novartis interview, you must be prepared to discuss your past work with exceptional depth and clarity. The evaluation is heavily indexed on practical application, system design, and behavioral competencies.

Resume & Past Project Deep Dive

Your past experience is the primary lens through which Novartis evaluates your technical capabilities. Interviewers will meticulously review the projects listed on your resume, probing for your specific contributions, the challenges you faced, and the impact of your work. Strong performance means being able to articulate the entire lifecycle of a project, from the initial problem statement to the final deployment and monitoring phases.

Be ready to go over:

• End-to-end model development – Explaining how you gathered data, selected features, trained models, and evaluated performance.
• API design and integration – Discussing how you exposed your models for consumption by other services or front-end applications.
• Overcoming technical roadblocks – Detailing specific instances where a project failed or stalled, and the technical steps you took to resolve the issue.
• Advanced concepts (less common) –
  • Model optimization for low-latency environments.
  • Handling highly imbalanced or noisy datasets (common in healthcare).
  • Versioning and monitoring of deployed machine learning models.

Example questions or scenarios:

• "Walk me through the most complex machine learning pipeline you built in your last role. What were the primary bottlenecks?"
• "Explain how you designed the API for the project listed on your resume. How did you handle authentication and rate limiting?"
• "Tell me about a time your model underperformed in production. How did you diagnose and fix the issue?"

Project Workflow & System Design

Novartis operates at an enterprise scale, meaning your models must be robust, scalable, and maintainable. This area evaluates your ability to design systems that work in the real world, not just in a Jupyter notebook. You may be asked to participate in a project workflow discussion or deliver a presentation detailing how you would architect a specific solution.

Be ready to go over:

• High-level architecture – Mapping out data ingestion, storage, compute, and serving layers.
• MLOps and deployment strategies – Explaining how you handle CI/CD for machine learning, containerization, and orchestration.
• Scalability and reliability – Designing systems that can handle increasing data volumes while maintaining high availability.
• Advanced concepts (less common) –
  • Federated learning approaches for privacy-preserving AI.
  • Designing architectures compliant with healthcare data regulations (e.g., HIPAA, GDPR).

Example questions or scenarios:

• "How would you design an end-to-end workflow to predict patient readmission rates using historical clinical data?"
• "During your presentation, the panel asks: 'How will this architecture scale if our data volume increases tenfold next year?' How do you respond?"
• "Describe your approach to transitioning a model from a research prototype to a production-ready API."

Stakeholder Management & Behavioral Alignment

Building great AI is only half the job; the other half is ensuring it gets adopted. Novartis heavily evaluates your ability to work with non-technical stakeholders, manage expectations, and drive alignment. Strong candidates will demonstrate empathy, clear communication, and a track record of successfully navigating organizational complexity.

Be ready to go over:

• Translating technical concepts – Explaining complex AI mechanisms to business leaders or medical professionals in plain language.
• Handling pushback – Managing situations where stakeholders disagree with your approach or have unrealistic expectations about what AI can achieve.
• Cross-functional collaboration – Highlighting your experience working alongside product managers, data engineers, and domain experts.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex machine learning concept to a non-technical stakeholder who was skeptical of your approach."
• "Describe a situation where a project's requirements kept changing. How did you manage the stakeholder relationships while keeping the project on track?"
• "How do you ensure that the AI solutions you build actually solve the underlying business problem?"