To succeed, you must excel across several distinct evaluation dimensions. Eli Lilly and interviewers are thorough, and they will probe both your technical depth and your collaborative instincts.
Resume Deep Dive and Technical Validation
Interviewers at Eli Lilly and heavily anchor their technical questions to the skills and experiences you explicitly list on your resume. If you claim proficiency in a tool or method, you must be prepared to defend it.
- Why it matters: Honesty and self-awareness are critical in a highly regulated industry. Interviewers need to know that your stated capabilities match your actual execution level.
- What strong performance looks like: A strong candidate can clearly articulate the technical architecture of past projects, explain why specific tools (like Python or SQL) were chosen, and discuss the limitations of their approach.
Be ready to go over:
- Programming Fundamentals – Basic to intermediate programming questions, usually in Python or the specific language mentioned in the job description.
- Tool-Specific Inquiries – Questions about specific libraries (e.g., Pandas, NumPy) or visualization tools (e.g., Tableau, PowerBI) you have used.
- Data Cleaning and Manipulation – How you handle missing data, outliers, or messy datasets in your previous roles.
- Advanced concepts (less common) – Predictive modeling, basic machine learning pipelines, or advanced statistical programming.
Example questions or scenarios:
- "Walk me through a time you used Python to automate a data pipeline. What libraries did you use and why?"
- "Explain how you handled a dataset with significant missing values in your last project."
- "Write a basic script or query to aggregate this dataset by region and calculate the moving average."
Statistical Knowledge and Analysis Planning
You will be expected to think like a true analyst, not just a coder. This means understanding the underlying statistics and knowing how to design an experiment or analysis.
- Why it matters: Eli Lilly and makes decisions that affect patient health and massive financial investments. Flawed analysis plans can lead to incorrect conclusions.
- What strong performance looks like: You should be able to take an ambiguous prompt, ask clarifying questions, and outline a step-by-step statistical approach to solve it.
Be ready to go over:
- A/B Testing and Experimentation – Designing tests, defining control/treatment groups, and determining statistical significance.
- Descriptive and Inferential Statistics – Understanding variance, p-values, confidence intervals, and distributions.
- Case Structuring – Building an end-to-end analysis plan from a high-level business problem.
Example questions or scenarios:
- "We are launching a new internal tool for our sales team. Give me a proper analysis plan to determine if it is increasing their efficiency."
- "How would you explain a p-value to a marketing manager?"
- "Walk me through how you would determine if a sudden drop in a key metric is a statistical anomaly or a real business issue."
Cross-Functional Collaboration and Behavioral Fit
Your ability to work with others is just as important as your technical skills. The company relies heavily on the STAR method (Situation, Task, Action, Result) to evaluate your soft skills.
- Why it matters: Data Analysts frequently partner with stakeholders who have zero data background. You must be an effective translator and a diplomatic collaborator.
- What strong performance looks like: Providing structured, concise answers that highlight your empathy, leadership, and ability to navigate conflict productively.
Be ready to go over:
- Stakeholder Management – Translating technical constraints to non-technical audiences.
- Conflict Resolution – Handling disagreements about data interpretation or project prioritization.
- Leadership and Initiative – Times when you stepped up to guide a team or project.
Example questions or scenarios:
- "Tell me about a time you worked with someone without data experience. How did you ensure they understood your findings?"
- "Describe a situation where you got into a conflict with a cross-functional team member. How did you resolve it?"
- "Give an example of a time you had to push back on a stakeholder's request because the data didn't support their hypothesis."
Logic and Problem-Solving
Sometimes referred to by candidates as "IQ testing questions," these are logic puzzles, guesstimates, or structured thinking exercises.
- Why it matters: These questions test your mental agility and how you approach problems when you lack historical data.
- What strong performance looks like: Breaking the problem down logically, stating your assumptions clearly, and arriving at a reasonable conclusion without getting flustered.
Be ready to go over:
- Guesstimates – Estimating market sizes or operational metrics using logical proxies.
- Process Troubleshooting – Finding the root cause of a hypothetical systemic breakdown.
Example questions or scenarios:
- "How would you estimate the number of pharmacies in a specific state without looking it up?"
- "If our primary data pipeline fails, what are the first three logical steps you take to identify the bottleneck?"
