Eli Lilly And Company Data Analyst Interview Questions + Guide in 2024

Eli Lilly and Company is a global healthcare leader headquartered in Indianapolis, Indiana. With a mission to unite caring with discovery, Lilly focuses on discovering and delivering life-changing medicines to patients worldwide. The company places high importance on improving the understanding and management of diseases and contributing to communities through philanthropy and volunteerism.

As a Data Analyst at Lilly, you’ll be part of the Advanced Analytics and Data Sciences team. Your role will require a blend of technical expertise in data analytics, proficiency in programming languages like Python and SQL, and strong problem-solving skills. You’ll work closely with business and research teams to deliver valuable insights that drive better decision-making.

This guide will provide you with insights into the interview process, commonly asked Eli Lilly and Company data analyst interview questions, and tips on how to succeed. Welcome to the first step of your journey with Lilly through Interview Query!

The interview process usually depends on the role and seniority; however, you can expect the following on a Eli Lilly and Company data analyst interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Eli Lilly Talent Acquisition Team will contact you and verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the Eli Lilly Data Analyst hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Eli Lilly data analyst role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Eli Lilly’s data systems, ETL pipelines, and SQL queries.

In the case of data analyst roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Eli Lilly office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the data analyst role at Eli Lilly.

Typically, interviews at Eli Lilly and Company vary by role and team, but commonly, Data Analyst interviews follow a fairly standardized process across these question topics.

1. Create a function max_substring to find the maximal substring shared by two strings.

Given two strings, string1 and string2, write a function max_substring to return the maximal substring shared by both strings. If there are multiple max substrings with the same length, return any one of them.

2. Develop a function moving_window to find the moving window average of a list of numbers.

Given a list of numbers nums and an integer window_size, write a function moving_window to find the moving window average.

3. Write a function to determine if a string is a palindrome.

Given a string, write a function to determine if it is a palindrome. A palindrome reads the same forwards and backward.

4. Write a query to find users currently “Excited” and never “Bored” with a campaign.

You have a table of users’ impressions of ad campaigns over time. Each impression_id consists of values of user engagement specified by Excited, OK, and Bored. Write a query to find all users that are currently “Excited” and have never been “Bored” with a campaign.

5. Create a function search_list to check if a target value is in a linked list.

Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with value and next keys. If the linked list is empty, you’ll receive None.

6. What is the downside of only using the R-squared ((R^2)) value to determine a relationship between two variables?

You are analyzing how well a model fits the data and want to determine a relationship between two variables. What are the limitations of relying solely on the R-squared value?

7. Is a coin that comes up tails 8 times and heads twice in 10 flips fair?

You flip a coin 10 times, resulting in 8 tails and 2 heads. Is this coin fair?

8. How would you explain what a p-value is to someone who is not technical?

Describe what a p-value is in simple terms for someone without a technical background.

9. What’s the probability that (2X > Y) given two independent standard normal random variables (X) and (Y)?

Given two independent standard normal random variables (X) and (Y), calculate the probability that (2X > Y).

10. How would you build a fraud detection model using a dataset of 600,000 credit card transactions?

Imagine you work at a major credit card company and are given a dataset of 600,000 credit card transactions. Describe your approach to building a fraud detection model in the comments.

11. How does random forest generate the forest and why use it over logistic regression?

Explain the process of how a random forest generates its forest. Additionally, discuss why one might choose random forest over other algorithms such as logistic regression.

12. When would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms. Provide an example of when you would use a bagging algorithm versus a boosting algorithm, and discuss the tradeoffs between the two.

13. How would you evaluate and compare two credit risk models for personal loans?

1. Identify the type of model your co-worker developed to determine loan eligibility.
2. Given that personal loans are monthly installments, describe how you would measure the difference between two credit risk models within a timeframe.
3. List the metrics you would track to measure the success of the new model.

14. How would you explain linear regression to different audiences?

Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Tailor your explanations to the understanding level of each audience.

15. Would you think there was anything fishy about the results of an A/B test with 20 variants?

Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with these results?

16. What considerations should be made when testing hundreds of hypotheses with many t-tests?

You are testing hundreds of hypotheses using multiple t-tests. What factors should you consider to ensure the validity of your results?

17. How would you generate a daily report and evaluate campaign performance for the first 7 days?

Given a schema representing advertiser campaigns and impressions, generate a daily report for the first 7 days. Evaluate campaign performance and identify which promos need attention using a specific heuristic.

18. How would you investigate if a redesigned email campaign led to an increase in conversion rates?

A new marketing manager redesigned the new-user email journey, and conversion rates increased from 40% to 43%. However, the rate was previously 45% before dropping to 40%. How would you determine if the redesign caused the increase?

19. What kind of analysis would you conduct to recommend UI changes for a community forum app?

You have access to tables summarizing user event data for a community forum app. What analysis would you perform to recommend improvements to the user interface?

Here are a few tips for acing your Eli Lilly interview:

1. Understand Eli Lilly’s Mission: Familiarize yourself with Eli Lilly’s mission and core values. Be prepared to discuss how your skills and experiences align with their objectives, particularly in the pharmaceutical and healthcare sectors.

2. Be Data-Driven: Eli Lilly places a strong emphasis on data-driven decision-making. Brush up on your knowledge of statistics, SQL, and data visualization tools like Tableau or PowerBI.

3. Technical and Behavioral Proficiency: Be ready for a mix of technical questions related to Python, R, SQL, and data analysis. Expect behavioral questions to understand your problem-solving abilities, team collaboration, and adaptability.

What is the average salary for a Data Analyst at Eli Lilly And Company?

According to Glassdoor, data analysts at Eli Lilly and Company earn between $79K to $120K per year, with an average of $97K per year.

What key skills are required for a Data Analyst role at Eli Lilly and Company?

For a Data Analyst role at Eli Lilly and Company, proficiency in Python and SQL is essential. Additional skills include data visualization, data storytelling, web-based data visualization using d3, and familiarity with tools like SAP and MES. Understanding advanced analytics methods, machine learning, and statistical techniques is also important.

How does Eli Lilly and Company support career growth for Data Analysts?

Eli Lilly and Company fosters career growth through continuous learning, feedback, and collaboration. The company encourages data analysts to stay current with the latest methods, participate in design reviews, and be actively involved in problem-solving with diverse business partners. There are also numerous employee resource groups and opportunities for internal and external consulting experience.

What is the company culture like at Eli Lilly and Company?

Eli Lilly and Company prides itself on a culture that combines caring with discovery. The company focuses on improving global health, valuing innovation, and giving back to communities. Employees are encouraged to put people first, collaborate effectively, and strive for excellence in their work.

The interview process for the Data Analyst position at Eli Lilly and Company is systematic and straightforward, focusing on both technical capabilities and soft skills.

If you want more insights about the company, check out our main Eli Lilly And Company Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Eli Lilly and Company’s interview process for different positions.

You can also check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!