Interview Guides

Eli Lilly and Data Scientist Interview Guide 2026

Eli Lilly and

Data Scientist

What is a Data Scientist at Eli Lilly and?

The Data Scientist role at Eli Lilly and is pivotal in harnessing data to drive decision-making and innovation in pharmaceuticals. By analyzing complex datasets, Data Scientists contribute to the development of groundbreaking treatments and therapies that improve patient outcomes. You will be part of a multidisciplinary team that collaborates across various functions, including research and development, marketing, and regulatory affairs, making your contributions vital to the company's mission of improving healthcare globally.

In this role, you can expect to work on a variety of challenges, from predictive modeling and statistical analysis to machine learning applications that enhance operational efficiencies and product effectiveness. The dynamic environment at Eli Lilly and provides opportunities to engage with cutting-edge technology and methodologies, ultimately playing a critical role in the development of solutions that can transform patient care.

Common Interview Questions

As you prepare for your interviews, expect questions that reflect both technical expertise and behavioral insights. These questions are drawn from candidates' experiences and may vary by team. The goal is to illustrate common patterns rather than provide a memorization list.

Technical / Domain Questions

This category focuses on your understanding of data science methodologies and your ability to apply them effectively.

Explain the significance of p-values in hypothesis testing.
How would you implement a linear regression model? What are its assumptions?
Describe the differences between supervised and unsupervised learning.
What techniques would you use to handle missing data in a dataset?
Can you discuss a project where you applied machine learning? What was your approach?

Behavioral / Leadership

Behavioral questions assess your past experiences and how they relate to the core competencies necessary for success at Eli Lilly and.

Describe a challenging project and how you overcame obstacles.
How do you prioritize multiple tasks with tight deadlines?
Give an example of how you worked effectively in a team setting.
Explain a time when you had to communicate complex information to a non-technical audience.

Problem-Solving / Case Studies

Expect case study questions that require you to demonstrate your analytical thinking and problem-solving capabilities.

How would you approach a project aimed at optimizing a drug marketing strategy?
Given a dataset on patient outcomes, what steps would you take to analyze the data and draw insights?

Coding / Algorithms

Coding questions may be included, depending on the specific requirements of the team.

Write a function to calculate the correlation coefficient between two variables.
How would you implement a decision tree algorithm from scratch?

See every interview question for this role

Practice questions from our question bank

Curated questions for Eli Lilly and 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

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

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

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 succeeding in the interview process at Eli Lilly and. You should focus on demonstrating both your technical skills and your ability to work within a team in a fast-paced environment.

Role-related Knowledge – This criterion encompasses your technical expertise in data science, including familiarity with statistical methods, machine learning algorithms, and data processing tools. Interviewers will look for practical examples from your past work that showcase your skill set.

Problem-Solving Ability – Your capacity to analyze problems and synthesize solutions will be evaluated. Be prepared to discuss your thought process and the methodologies you employ when faced with new challenges.

Leadership – While you may not be in a formal leadership position, your ability to influence and collaborate with others is critical. Highlight instances where you’ve taken initiative, mentored others, or successfully led a project.

Culture Fit / Values – Eli Lilly and values teamwork, innovation, and a commitment to patient care. Showcase how your personal values align with the company’s mission and how you would contribute to a positive workplace culture.

Interview Process Overview

The interview process at Eli Lilly and typically involves multiple stages, starting with an initial screening followed by a series of interviews that assess both technical and behavioral competencies. Candidates can expect a structured process where each step builds on the previous one, allowing for a comprehensive evaluation of your fit for the role.

Throughout the interviews, you will interact with various team members, including engineers and hiring managers, who will assess your technical skills, cultural fit, and problem-solving abilities. The emphasis is on collaboration and ensuring that candidates align with the company's values and mission.

The visual timeline shows the typical stages of the interview process, including initial screenings, technical interviews, and final behavioral assessments. Use this timeline to plan your preparation and manage your energy throughout the process. Remember, each interview is an opportunity to showcase your skills and demonstrate your enthusiasm for the role.

Deep Dive into Evaluation Areas

Understanding how you will be evaluated is crucial to your preparation. Here are key evaluation areas that are particularly relevant for a Data Scientist at Eli Lilly and.

Technical Proficiency

Technical proficiency is paramount for success in this role, as you will be expected to apply advanced data analysis techniques to real-world problems.

Statistical Analysis – Familiarity with statistical methods and their applications in research.
Machine Learning – Understanding various algorithms and when to apply them effectively.
Programming Languages – Proficiency in languages such as Python or R for data manipulation and analysis.

Example questions:

"What is your experience with machine learning libraries in Python?"
"How do you validate the results of your analysis?"

Analytical Thinking

Your ability to think critically about data and draw actionable insights is essential.

Data Interpretation – Skills in interpreting data trends and making informed decisions.
Problem-Solving – Approaches to tackling complex problems with innovative solutions.

Example scenarios:

"How would you analyze a dataset to determine the efficacy of a new drug?"
"Describe your approach to a situation where your analysis did not yield expected results."

Communication Skills

Being able to convey complex information in a clear and understandable manner is crucial, especially when collaborating with cross-functional teams.

Presentation Skills – Effectively presenting findings to stakeholders.
Collaborative Communication – Working with team members from diverse backgrounds.

Example questions:

"How do you ensure your findings are understood by non-technical stakeholders?"
"Can you provide an example of a time you received feedback on your presentation?"

Key Responsibilities

As a Data Scientist at Eli Lilly and, your day-to-day responsibilities will include a mix of analysis, collaboration, and project management. You will engage in data collection, modeling, and interpretation, contributing to projects that drive strategic decisions.

Collaborating with cross-functional teams, you will analyze data to support clinical trials, marketing strategies, and operational efficiencies. Typical projects may involve developing predictive models to assess patient outcomes or optimizing marketing campaigns based on data insights.

Your role will be central to ensuring that data-driven insights inform product development and operational strategies, ultimately impacting patient care positively.

Role Requirements & Qualifications

To be competitive for the Data Scientist position at Eli Lilly and, candidates should possess a blend of technical and interpersonal skills.

Must-have skills:
- Strong foundation in statistics and machine learning concepts.
- Proficiency in programming languages such as Python or R.
- Experience with data visualization tools.
Nice-to-have skills:
- Familiarity with big data technologies (e.g., Hadoop, Spark).
- Experience in the pharmaceutical or healthcare industry.
- Knowledge of regulatory standards related to data.

Frequently Asked Questions

Q: What is the typical interview difficulty for this role?
The interview process is generally considered average in difficulty. Candidates should be well-prepared for both technical and behavioral questions.

Q: How much preparation time is typical?
Most candidates recommend spending at least a few weeks preparing, focusing on technical skills and reviewing past projects.

Q: What differentiates successful candidates?
Successful candidates often demonstrate not only technical proficiency but also strong communication skills and a clear alignment with Eli Lilly and’s core values.

Q: How long is the typical timeline from initial screen to offer?
The interview process can take anywhere from a few weeks to a couple of months, depending on scheduling and team availability.

Q: Are there remote or hybrid work options for this role?
While some positions may offer flexibility, many roles, especially those involving collaboration, may require onsite presence in Indianapolis.

Other General Tips

Be prepared to discuss your past projects: Highlight specific examples from your experience that showcase your technical skills and problem-solving abilities.
Practice the STAR method: Structure your responses to behavioral questions using the Situation, Task, Action, Result framework for clarity and impact.
Understand Eli Lilly’s mission and values: Familiarize yourself with the company’s goals and how your work can contribute to improving patient outcomes.
Engage with your interviewers: Show genuine interest in their work and the team dynamics, as cultural fit is highly valued.

Tip

Remember to ask questions during your interviews. This demonstrates your interest and helps you assess if the company is the right fit for you.

Summary & Next Steps

The Data Scientist role at Eli Lilly and is not only an opportunity to apply your technical skills but also to make a meaningful impact on patient care and healthcare innovation. Prepare thoroughly by focusing on both technical competencies and behavioral insights, as these are crucial to your success in the interview process.

Key areas to concentrate on include your understanding of data science methodologies, analytical thinking, and effective communication. With dedicated preparation, you can enhance your performance and position yourself as a strong candidate.

For additional insights and resources, explore the wealth of information available on Dataford. Remember, your potential to succeed is within reach—focused preparation can significantly elevate your chances of landing this opportunity.

This salary data provides insight into the compensation range for the Data Scientist position, allowing you to align your expectations and negotiate effectively if offered a position.

See every interview question for this role

Eli Lilly and

Data Scientist

What is a Data Scientist at Eli Lilly and?

Common Interview Questions

Technical / Domain Questions

This category focuses on your understanding of data science methodologies and your ability to apply them effectively.

Explain the significance of p-values in hypothesis testing.
How would you implement a linear regression model? What are its assumptions?
Describe the differences between supervised and unsupervised learning.
What techniques would you use to handle missing data in a dataset?
Can you discuss a project where you applied machine learning? What was your approach?

Behavioral / Leadership

Behavioral questions assess your past experiences and how they relate to the core competencies necessary for success at Eli Lilly and.

Describe a challenging project and how you overcame obstacles.
How do you prioritize multiple tasks with tight deadlines?
Give an example of how you worked effectively in a team setting.
Explain a time when you had to communicate complex information to a non-technical audience.

Problem-Solving / Case Studies

Expect case study questions that require you to demonstrate your analytical thinking and problem-solving capabilities.

How would you approach a project aimed at optimizing a drug marketing strategy?
Given a dataset on patient outcomes, what steps would you take to analyze the data and draw insights?

Coding / Algorithms

Coding questions may be included, depending on the specific requirements of the team.

Write a function to calculate the correlation coefficient between two variables.
How would you implement a decision tree algorithm from scratch?

See every interview question for this role

Practice questions from our question bank

Curated questions for Eli Lilly and 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

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

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

Sign up to see all questions

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

Getting Ready for Your Interviews

Interview Process Overview

Deep Dive into Evaluation Areas

Understanding how you will be evaluated is crucial to your preparation. Here are key evaluation areas that are particularly relevant for a Data Scientist at Eli Lilly and.

Technical Proficiency

Technical proficiency is paramount for success in this role, as you will be expected to apply advanced data analysis techniques to real-world problems.

Statistical Analysis – Familiarity with statistical methods and their applications in research.
Machine Learning – Understanding various algorithms and when to apply them effectively.
Programming Languages – Proficiency in languages such as Python or R for data manipulation and analysis.

Example questions:

"What is your experience with machine learning libraries in Python?"
"How do you validate the results of your analysis?"

Analytical Thinking

Your ability to think critically about data and draw actionable insights is essential.

Data Interpretation – Skills in interpreting data trends and making informed decisions.
Problem-Solving – Approaches to tackling complex problems with innovative solutions.

Example scenarios:

"How would you analyze a dataset to determine the efficacy of a new drug?"
"Describe your approach to a situation where your analysis did not yield expected results."

Communication Skills

Being able to convey complex information in a clear and understandable manner is crucial, especially when collaborating with cross-functional teams.

Presentation Skills – Effectively presenting findings to stakeholders.
Collaborative Communication – Working with team members from diverse backgrounds.

Example questions:

"How do you ensure your findings are understood by non-technical stakeholders?"
"Can you provide an example of a time you received feedback on your presentation?"

Key Responsibilities

Your role will be central to ensuring that data-driven insights inform product development and operational strategies, ultimately impacting patient care positively.

Role Requirements & Qualifications

To be competitive for the Data Scientist position at Eli Lilly and, candidates should possess a blend of technical and interpersonal skills.

Must-have skills:
- Strong foundation in statistics and machine learning concepts.
- Proficiency in programming languages such as Python or R.
- Experience with data visualization tools.
Nice-to-have skills:
- Familiarity with big data technologies (e.g., Hadoop, Spark).
- Experience in the pharmaceutical or healthcare industry.
- Knowledge of regulatory standards related to data.

Frequently Asked Questions

Q: How much preparation time is typical?
Most candidates recommend spending at least a few weeks preparing, focusing on technical skills and reviewing past projects.

Q: How long is the typical timeline from initial screen to offer?
The interview process can take anywhere from a few weeks to a couple of months, depending on scheduling and team availability.

Other General Tips

Be prepared to discuss your past projects: Highlight specific examples from your experience that showcase your technical skills and problem-solving abilities.
Practice the STAR method: Structure your responses to behavioral questions using the Situation, Task, Action, Result framework for clarity and impact.
Understand Eli Lilly’s mission and values: Familiarize yourself with the company’s goals and how your work can contribute to improving patient outcomes.
Engage with your interviewers: Show genuine interest in their work and the team dynamics, as cultural fit is highly valued.

Tip

Remember to ask questions during your interviews. This demonstrates your interest and helps you assess if the company is the right fit for you.

Summary & Next Steps

This salary data provides insight into the compensation range for the Data Scientist position, allowing you to align your expectations and negotiate effectively if offered a position.