Interview Guides

Novartis

Data Scientist

What is a Data Scientist at Novartis?

The role of a Data Scientist at Novartis is pivotal in harnessing data to drive innovation in healthcare solutions. As a Data Scientist, you will leverage advanced analytical techniques, machine learning, and statistical modeling to extract valuable insights from complex datasets. Your work will directly impact the development of products that enhance patient outcomes and contribute to Novartis’s mission of reimagining medicine.

In this role, you will collaborate with cross-functional teams, including biostatistics, clinical development, and digital health, to address real-world challenges in healthcare. The complexity of data derived from clinical trials, patient registries, and real-world evidence creates an exciting landscape where your analytical skills can flourish. You will be part of a dynamic environment that values creativity and strategic thinking, allowing you to influence critical decisions that shape the future of healthcare.

Common Interview Questions

As you prepare for your interview, expect a range of questions that reflect the competencies necessary for success at Novartis. The questions listed below are representative, drawn from 1point3acres.com, and will help you understand patterns in the types of inquiries you may encounter. Remember, the goal is not to memorize answers but to illustrate your thought process and problem-solving capabilities.

Technical / Domain Questions

This category evaluates your technical expertise and understanding of data science principles.

Explain the differences between supervised and unsupervised learning.
How do you handle missing data in a dataset?
What is regularization in machine learning, and why is it important?
Describe a project where you applied machine learning techniques to solve a business problem.
How would you assess the performance of a predictive model?

Behavioral / Leadership

These questions focus on your interpersonal skills and ability to work collaboratively within teams.

Describe a time when you faced a significant challenge in a project. How did you overcome it?
How do you prioritize tasks when working on multiple projects?
Give an example of how you have worked effectively within a diverse team.
How do you handle feedback and criticism from peers or managers?
What motivates you to succeed in your role as a Data Scientist?

Problem-Solving / Case Studies

Expect to encounter case studies or problem-solving scenarios that assess your analytical thinking.

How would you approach a situation where your model's predictions are significantly off?
Given a set of data, how would you derive actionable insights for a product launch?
Walk us through your thought process in designing an experiment to test a new drug.
Describe how you would evaluate the impact of a new healthcare intervention using analytical methods.
If tasked with improving patient adherence to medication, what data would you analyze?

Coding / Algorithms

If coding skills are a part of the role, be prepared for algorithm-related questions.

Write a function to calculate the mean and median of a list of numbers.
How would you implement a decision tree algorithm from scratch?
Can you explain the time complexity of different sorting algorithms?
Given a dataset, demonstrate how you would perform exploratory data analysis (EDA).
Write a SQL query to extract specific information from a database.

See every interview question for this role

Practice questions from our question bank

Curated questions for Novartis from real interviews. Click any question to practice and review the answer.

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Pipelines

Handle Missing Values in ETL

Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.

ETL

Data Wrangling

Quality

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Easy

SQL & Data Manipulation

Handling Missing Values in SQL

Explain how to detect and handle NULL values in SQL using filtering, COALESCE, CASE, and business-aware imputation.

Aggregations

Case When

Data Wrangling

Easy

Model Evaluation

Explain Cross-Validation to Executives

Explain why cross-validation gives a more trustworthy view of model performance than a single strong test split.

Cross-Validation

Accuracy

Calibration

Easy

Model Evaluation

Compare Precision-Recall Tradeoffs

Compare two classifiers with high-precision vs high-recall behavior and recommend the better model under business cost and review-capacity constraints.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate F1 Score Significance in Model Performance

Analyze the significance of the F1 score in a binary classification model for customer churn prediction, and propose improvements.

Accuracy

F1 Score

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Pipelines

Build Data Quality Controls Pipeline

Design a batch ETL pipeline that validates CRM, billing, and product data before loading curated Snowflake tables.

Data Modeling

ETL

Quality

Easy

SQL & Data Manipulation

Classify Orders with CASE WHEN

Explain how CASE WHEN adds conditional logic to SQL queries for labeling, transforming, and aggregating data.

Aggregations

Case When

Data Wrangling

Easy

Pipelines

Ensure Data Quality in ETL

Design a Snowflake ETL pipeline that enforces schema, deduplication, reconciliation, and auditable data quality checks for finance data.

Data Modeling

ETL

Quality

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Model Evaluation

Evaluate Model Metrics for Customer Churn Prediction

Analyze why a customer churn prediction model has low recall despite high precision and propose actionable improvements.

Easy

Metrics

Measure Checkout Funnel Conversion Rate

Define overall and step-level funnel conversion for an e-commerce checkout flow and explain how to diagnose where drop-off occurs.

KPIs

Conversion Rate

Funnel Analysis

Easy

SQL & Data Manipulation

Handling Missing Demographic Data

Explain how to assess, quantify, and handle missing demographic fields in SQL without distorting downstream analysis.

Subqueries

Case When

Data Wrangling

Easy

SQL & Data Manipulation

Detect and Handle Outliers in SQL

Explain common SQL-friendly ways to detect outliers and how to handle them without distorting downstream analysis.

Aggregations

Group By

Data Wrangling

Easy

Machine Learning

Compare Bagging and Boosting for Claims Risk

Explain and compare bagging vs boosting by training tree-based ensembles to predict high-cost insurance claims.

Ensemble Methods

Bias-Variance Tradeoff

Decision Trees

Easy

Statistics & Probability

A/B Test for Ranking Model Value

Use a two-proportion z-test to determine whether a new ranking model significantly improves recommendation CTR in an A/B test.

A/B Testing

Statistical Significance

Experimentation

Easy

Model Evaluation

Choose Metrics for Business Impact

Decide whether precision, recall, F1-score, or RMSE best fits fraud detection and demand forecasting given asymmetric business costs.

Accuracy

Precision

Recall

+2 more

Medium

Product Sense

Investigate Engagement Drop with Stable ARR

Analyze the root cause of a 10% engagement drop while ARR remains flat to inform product strategy.

User Needs

Engagement Metrics

Churn

+2 more

Sign up to see all questions

Create a free account to access every interview question for this role.

Getting Ready for Your Interviews

Preparation is key to excelling in your interviews at Novartis. Understanding the evaluation criteria that interviewers prioritize can help you present your skills effectively.

Role-related knowledge – This criterion assesses your understanding of data science concepts, methodologies, and tools relevant to the role. Be prepared to discuss your technical expertise and how it aligns with the specific needs of Novartis.

Problem-solving ability – Interviewers will evaluate how you approach and structure challenges. Demonstrating a logical thought process and the ability to work through complex problems will be crucial.

Leadership – Your capacity to communicate, influence, and collaborate with others will be under scrutiny. Showcase your ability to lead initiatives and work effectively in teams.

Culture fit / values – Novartis emphasizes a culture of innovation, collaboration, and patient-centricity. Be ready to discuss how your values align with those of the company.

Interview Process Overview

The interview process for a Data Scientist at Novartis is designed to assess both your technical capabilities and your cultural fit within the organization. The process typically begins with an initial screening call, usually conducted by HR, where you may discuss your background and experiences. This is followed by a series of interviews that can include technical assessments, case studies, and behavioral questions.

Candidates often experience a blend of interactions, including discussions with hiring managers and potential team members. Expect a mix of in-depth technical questions and situational discussions aimed at understanding how you work within teams and approach problem-solving. The emphasis on collaboration and impact is a hallmark of the Novartis interview philosophy, making it essential for candidates to demonstrate their alignment with these values.

The visual timeline provides an overview of the various stages in the interview process, including screening calls, technical assessments, and final interviews. Use this timeline to strategize your preparation and manage your energy effectively throughout the process. Note that experiences may vary based on the specific team or location.

Deep Dive into Evaluation Areas

Understanding how you will be evaluated during the interview process is crucial. Below are key evaluation areas that are particularly important for the Data Scientist role at Novartis.

Technical Expertise

Technical expertise is fundamental for a Data Scientist. You will be evaluated on your knowledge of data science principles and methodologies.

Be prepared to discuss your proficiency in various data manipulation and analysis tools such as Python, R, SQL, and relevant libraries like Pandas and Scikit-learn.
Expect questions that gauge your understanding of statistical techniques and machine learning algorithms, as well as your ability to apply them to real-world scenarios.

Analytical Skills

Your analytical skills will be assessed through problem-solving questions and case studies.

Be ready to demonstrate how you approach data analysis, including your process for cleaning, exploring, and interpreting data.
Interviewers will look for your ability to extract actionable insights from complex datasets and communicate your findings effectively.

Communication and Collaboration

This area evaluates how well you can work with others and convey your ideas.

Highlight experiences where you have successfully collaborated with cross-functional teams or communicated complex technical concepts to non-technical stakeholders.
Strong candidates will illustrate their ability to influence and engage others in discussions around data-driven decision-making.

Innovation and Impact

Demonstrating a mindset focused on innovation and impact is crucial at Novartis.

Be prepared to discuss projects where you implemented new methodologies or contributed to significant improvements.
Interviewers will be interested in how your work has directly impacted business outcomes or patient care.

Advanced Concepts

Less common but noteworthy topics may arise to differentiate strong candidates.

Expect questions related to advanced machine learning techniques, deep learning frameworks, or emerging trends in data science.
Be ready to discuss your thoughts on ethical considerations in data science, particularly in healthcare.

Key Responsibilities

In your role as a Data Scientist at Novartis, you will be responsible for various critical tasks that contribute to the company's mission. Your day-to-day responsibilities will include:

Analyzing complex datasets to identify trends and patterns that inform business decisions and product development.
Collaborating with cross-functional teams to design and implement data-driven solutions that enhance patient outcomes.
Developing and validating predictive models that assist in clinical trials and healthcare interventions.
Communicating findings and insights through comprehensive reports and presentations, making data accessible to stakeholders at all levels.
Continuously exploring new technologies and methodologies to enhance data analysis capabilities and drive innovation within the organization.

Role Requirements & Qualifications

To be considered a strong candidate for the Data Scientist position at Novartis, you should possess a combination of technical and soft skills:

Must-have skills:
- Proficiency in programming languages such as Python or R.
- Strong understanding of statistical analysis and machine learning algorithms.
- Experience with data visualization tools (e.g., Tableau, Power BI).
- Knowledge of SQL for data manipulation and extraction.
Nice-to-have skills:
- Familiarity with big data technologies (e.g., Hadoop, Spark).
- Experience in the healthcare or pharmaceutical industry.
- Knowledge of cloud-based data services (e.g., AWS, Azure).
- Advanced degree (Master's or PhD) in a quantitative field.

Frequently Asked Questions

Q: What is the typical difficulty level of interviews at Novartis?
The interviews for Data Scientist roles at Novartis are generally considered average to difficult, depending on the specific team and the technical depth of questions. Candidates should be well-prepared to discuss both technical and behavioral aspects.

Q: How can I differentiate myself as a candidate?
Successful candidates often showcase a combination of strong technical skills and the ability to communicate their insights effectively. Demonstrating a passion for healthcare and an understanding of how data can drive innovation will also set you apart.

Q: What is the company culture like at Novartis?
Novartis fosters a culture of collaboration, innovation, and patient-centricity. Employees are encouraged to think creatively and work together toward common goals, making it essential to highlight your teamwork experiences during interviews.

Q: How long does the interview process typically take?
The timeline from initial screening to an offer can vary, but candidates should expect a few weeks to a couple of months, depending on scheduling and team dynamics.

Q: Are there remote work opportunities for this role?
Novartis has embraced flexible working arrangements, including remote and hybrid options. However, the specifics may vary based on team requirements and location.

Other General Tips

Tailor your answers: Make sure your responses are relevant to the role and company values at Novartis. Relate your experiences directly to the challenges and goals of the organization.
Showcase real-world applications: When discussing your projects, focus on the real-world impact of your work, particularly in the context of healthcare and patient outcomes.
Prepare for case studies: Practice solving case studies that require analytical thinking and the application of data science principles to ensure you're comfortable during the interview.
Engage with your interviewers: Use the opportunity to ask thoughtful questions that demonstrate your curiosity and engagement with the role and the organization.

Summary & Next Steps

The Data Scientist position at Novartis represents an exciting opportunity to leverage data science in the healthcare sector, contributing to meaningful advancements in patient care. As you prepare, focus on the evaluation areas discussed, familiarize yourself with common interview questions, and position your experiences to align with the needs of the role.

Remember that effective preparation can significantly enhance your performance. Utilize available resources, such as Dataford, to gather additional insights and refine your approach. Your potential to succeed as a Data Scientist at Novartis lies in your ability to demonstrate both technical expertise and a commitment to improving healthcare outcomes.

Sign up to read the full guide

Create a free account to unlock the complete interview guide with all sections.

Novartis

Data Scientist

Build my plan

What is a Data Scientist at Novartis?

Common Interview Questions

Technical / Domain Questions

This category evaluates your technical expertise and understanding of data science principles.

Explain the differences between supervised and unsupervised learning.
How do you handle missing data in a dataset?
What is regularization in machine learning, and why is it important?
Describe a project where you applied machine learning techniques to solve a business problem.
How would you assess the performance of a predictive model?

Behavioral / Leadership

These questions focus on your interpersonal skills and ability to work collaboratively within teams.

Describe a time when you faced a significant challenge in a project. How did you overcome it?
How do you prioritize tasks when working on multiple projects?
Give an example of how you have worked effectively within a diverse team.
How do you handle feedback and criticism from peers or managers?
What motivates you to succeed in your role as a Data Scientist?

Problem-Solving / Case Studies

Expect to encounter case studies or problem-solving scenarios that assess your analytical thinking.

How would you approach a situation where your model's predictions are significantly off?
Given a set of data, how would you derive actionable insights for a product launch?
Walk us through your thought process in designing an experiment to test a new drug.
Describe how you would evaluate the impact of a new healthcare intervention using analytical methods.
If tasked with improving patient adherence to medication, what data would you analyze?

Coding / Algorithms

If coding skills are a part of the role, be prepared for algorithm-related questions.

Write a function to calculate the mean and median of a list of numbers.
How would you implement a decision tree algorithm from scratch?
Can you explain the time complexity of different sorting algorithms?
Given a dataset, demonstrate how you would perform exploratory data analysis (EDA).
Write a SQL query to extract specific information from a database.

See every interview question for this role

Practice questions from our question bank

Curated questions for Novartis from real interviews. Click any question to practice and review the answer.

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Pipelines

Handle Missing Values in ETL

Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.

ETL

Data Wrangling

Quality

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Easy

SQL & Data Manipulation

Handling Missing Values in SQL

Explain how to detect and handle NULL values in SQL using filtering, COALESCE, CASE, and business-aware imputation.

Aggregations

Case When

Data Wrangling

Easy

Model Evaluation

Explain Cross-Validation to Executives

Explain why cross-validation gives a more trustworthy view of model performance than a single strong test split.

Cross-Validation

Accuracy

Calibration

Easy

Model Evaluation

Compare Precision-Recall Tradeoffs

Compare two classifiers with high-precision vs high-recall behavior and recommend the better model under business cost and review-capacity constraints.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate F1 Score Significance in Model Performance

Analyze the significance of the F1 score in a binary classification model for customer churn prediction, and propose improvements.

Accuracy

F1 Score

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Pipelines

Build Data Quality Controls Pipeline

Design a batch ETL pipeline that validates CRM, billing, and product data before loading curated Snowflake tables.

Data Modeling

ETL

Quality

Easy

SQL & Data Manipulation

Classify Orders with CASE WHEN

Explain how CASE WHEN adds conditional logic to SQL queries for labeling, transforming, and aggregating data.

Aggregations

Case When

Data Wrangling

Easy

Pipelines

Ensure Data Quality in ETL

Design a Snowflake ETL pipeline that enforces schema, deduplication, reconciliation, and auditable data quality checks for finance data.

Data Modeling

ETL

Quality

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Model Evaluation

Evaluate Model Metrics for Customer Churn Prediction

Analyze why a customer churn prediction model has low recall despite high precision and propose actionable improvements.

Easy

Metrics

Measure Checkout Funnel Conversion Rate

Define overall and step-level funnel conversion for an e-commerce checkout flow and explain how to diagnose where drop-off occurs.

KPIs

Conversion Rate

Funnel Analysis

Easy

SQL & Data Manipulation

Handling Missing Demographic Data

Explain how to assess, quantify, and handle missing demographic fields in SQL without distorting downstream analysis.

Subqueries

Case When

Data Wrangling

Easy

SQL & Data Manipulation

Detect and Handle Outliers in SQL

Explain common SQL-friendly ways to detect outliers and how to handle them without distorting downstream analysis.

Aggregations

Group By

Data Wrangling

Easy

Machine Learning

Compare Bagging and Boosting for Claims Risk

Explain and compare bagging vs boosting by training tree-based ensembles to predict high-cost insurance claims.

Ensemble Methods

Bias-Variance Tradeoff

Decision Trees

Easy

Statistics & Probability

A/B Test for Ranking Model Value

Use a two-proportion z-test to determine whether a new ranking model significantly improves recommendation CTR in an A/B test.

A/B Testing

Statistical Significance

Experimentation

Easy

Model Evaluation

Choose Metrics for Business Impact

Decide whether precision, recall, F1-score, or RMSE best fits fraud detection and demand forecasting given asymmetric business costs.

Accuracy

Precision

Recall

+2 more

Medium

Product Sense

Investigate Engagement Drop with Stable ARR

Analyze the root cause of a 10% engagement drop while ARR remains flat to inform product strategy.

User Needs

Engagement Metrics

Churn

+2 more

Sign up to see all questions

Create a free account to access every interview question for this role.

Getting Ready for Your Interviews

Preparation is key to excelling in your interviews at Novartis. Understanding the evaluation criteria that interviewers prioritize can help you present your skills effectively.

Leadership – Your capacity to communicate, influence, and collaborate with others will be under scrutiny. Showcase your ability to lead initiatives and work effectively in teams.

Culture fit / values – Novartis emphasizes a culture of innovation, collaboration, and patient-centricity. Be ready to discuss how your values align with those of the company.

Interview Process Overview

Deep Dive into Evaluation Areas

Understanding how you will be evaluated during the interview process is crucial. Below are key evaluation areas that are particularly important for the Data Scientist role at Novartis.

Technical Expertise

Technical expertise is fundamental for a Data Scientist. You will be evaluated on your knowledge of data science principles and methodologies.

Be prepared to discuss your proficiency in various data manipulation and analysis tools such as Python, R, SQL, and relevant libraries like Pandas and Scikit-learn.
Expect questions that gauge your understanding of statistical techniques and machine learning algorithms, as well as your ability to apply them to real-world scenarios.

Analytical Skills

Your analytical skills will be assessed through problem-solving questions and case studies.

Be ready to demonstrate how you approach data analysis, including your process for cleaning, exploring, and interpreting data.
Interviewers will look for your ability to extract actionable insights from complex datasets and communicate your findings effectively.

Communication and Collaboration

This area evaluates how well you can work with others and convey your ideas.

Highlight experiences where you have successfully collaborated with cross-functional teams or communicated complex technical concepts to non-technical stakeholders.
Strong candidates will illustrate their ability to influence and engage others in discussions around data-driven decision-making.

Innovation and Impact

Demonstrating a mindset focused on innovation and impact is crucial at Novartis.

Be prepared to discuss projects where you implemented new methodologies or contributed to significant improvements.
Interviewers will be interested in how your work has directly impacted business outcomes or patient care.

Advanced Concepts

Less common but noteworthy topics may arise to differentiate strong candidates.

Expect questions related to advanced machine learning techniques, deep learning frameworks, or emerging trends in data science.
Be ready to discuss your thoughts on ethical considerations in data science, particularly in healthcare.

Key Responsibilities

In your role as a Data Scientist at Novartis, you will be responsible for various critical tasks that contribute to the company's mission. Your day-to-day responsibilities will include:

Analyzing complex datasets to identify trends and patterns that inform business decisions and product development.
Collaborating with cross-functional teams to design and implement data-driven solutions that enhance patient outcomes.
Developing and validating predictive models that assist in clinical trials and healthcare interventions.
Communicating findings and insights through comprehensive reports and presentations, making data accessible to stakeholders at all levels.
Continuously exploring new technologies and methodologies to enhance data analysis capabilities and drive innovation within the organization.

Role Requirements & Qualifications

To be considered a strong candidate for the Data Scientist position at Novartis, you should possess a combination of technical and soft skills:

Must-have skills:
- Proficiency in programming languages such as Python or R.
- Strong understanding of statistical analysis and machine learning algorithms.
- Experience with data visualization tools (e.g., Tableau, Power BI).
- Knowledge of SQL for data manipulation and extraction.
Nice-to-have skills:
- Familiarity with big data technologies (e.g., Hadoop, Spark).
- Experience in the healthcare or pharmaceutical industry.
- Knowledge of cloud-based data services (e.g., AWS, Azure).
- Advanced degree (Master's or PhD) in a quantitative field.

Frequently Asked Questions

Other General Tips

Tailor your answers: Make sure your responses are relevant to the role and company values at Novartis. Relate your experiences directly to the challenges and goals of the organization.
Showcase real-world applications: When discussing your projects, focus on the real-world impact of your work, particularly in the context of healthcare and patient outcomes.
Prepare for case studies: Practice solving case studies that require analytical thinking and the application of data science principles to ensure you're comfortable during the interview.
Engage with your interviewers: Use the opportunity to ask thoughtful questions that demonstrate your curiosity and engagement with the role and the organization.

Summary & Next Steps

Sign up to read the full guide

Create a free account to unlock the complete interview guide with all sections.