Candidates for statistics-focused roles at Eli Lilly describe resume deep-dives, statistical fundamentals, some coding, and behavioral questions. The format ranges from a single conversation to a full on-site day.
$138K
Avg. Base Comp
$165K
Avg. Total Comp
Not reported
Typical Rounds
4 weeks
Process Length
Candidates who interviewed for statistician and scientist roles at Eli Lilly describe quite different formats, so prepare for variety rather than one fixed loop. One reports a single 60-minute behavioral conversation with two interviewers who spoke with them one after the other. Another describes a 30-minute phone screen followed by a full-day on-site with six 45-minute interviews. A third went through an HR conversation, two technical rounds that included a time series assignment, and a behavioral finish, with about four weeks from application to decision.
The common thread is explaining your own work and statistical reasoning clearly. Candidates report resume walk-throughs, dissertation and research project deep-dives treated almost like peer review, and SAS and statistical fundamentals questions. One was asked to explain p-values to a clinician, which tests plain-language communication as much as theory. Another, in a 50-minute on-campus session, got basic algorithm, data structure and coding questions. The interviewers there cared more about the candidate's thought process and why they made certain choices than about simply reaching the right answer.
Behavioral questions show up too, from motivation and strengths and weaknesses to STAR-style teamwork prompts. Practice describing your methodology, assumptions and design choices as if a reviewer will push back. Outcomes in this evidence are mostly rejections, with one offer. The sample is small and spans several titles, so treat team-level details as likely to differ.
Synthesized from 5 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Eli Lilly And Company process.
I had a single behavioral round that lasted about 60 minutes with two interviewers, though they spoke with me sequentially rather than together. The conversation felt natural and grounded in my background. They started by walking through my resume, asking me to elaborate on specific projects and experiences I'd listed. Then they pivoted to motivation—why I was interested in the role and the company. What surprised me most were the more applied questions woven in between. One interviewer asked me to explain p-values to a clinician, which forced me to think about how to communicate statistical concepts to someone without a deep technical background. That was a good reminder that this role wasn't just about knowing the theory—it was about being able to translate it in practical contexts. The interviewers were conversational and gave me space to ask questions at the end, which felt like a genuine exchange rather than an interrogation. The whole vibe was fairly low-pressure compared to what I'd expected from a pharma company. They seemed interested in understanding my statistical thinking and how I approach problems, rather than grilling me on technical minutiae.
Prep tip from this candidate
Be ready to explain statistical fundamentals—especially assumptions underlying linear regression and how to communicate concepts like p-values to non-technical stakeholders. Practice translating your resume projects into conversational narratives that highlight statistical reasoning and real-world impact.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Eli Lilly And Company
Create top_ads with the top 3 ads and return the row counts for inner, left, right, and cross joins with ads
| Question | |
|---|---|
| Causal Email Journey | |
| Loan Model | |
| String Palindromes | |
| Client Solution Pushback | |
| Justify a Neural Network | |
| Your Strengths and Weaknesses | |
| Adam Optimizer | |
| 2nd Highest Salary | |
| Monthly Customer Report | |
| Cumulative Distribution | |
| Experiment Validity | |
| Last Transaction | |
| Weighted Keys | |
| Hurdles In Data Projects | |
| Brain Cancer Treatment Outcomes | |
| Always Excited Users | |
| Retailer Data Warehouse | |
| Total Spent on Products | |
| P-value to a Layman | |
| RMS Error | |
| Reducing Error Margin | |
| Detecting ECG Tachycardia Runs | |
| Fair Coin | |
| Cumulative Reset | |
| Time Difference | |
| Greatest Common Denominator | |
| Random Forest Explanation | |
| Subscription Retention | |
| Secret Wins |
Synthesized from candidate reports. Individual experiences may vary.
One candidate heard back about a week after applying with a phone screen invite. Another describes a 30-minute phone screen with technical questions anchored on their resume and relevant coursework. Be ready to summarize your background and the statistical foundation behind it.
Early conversations focus on your background. One candidate had an HR round that felt like a rigid checklist, while another had a 60-minute behavioral conversation with two interviewers, one after the other. It covered resume projects and why they wanted the role and company.
Reported technical content includes SAS and statistical fundamentals, a time series assignment, and basic algorithm, data structure and coding problems in a 50-minute on-campus session. Interviewers there probed why the candidate made certain choices, so explain your reasoning out loud rather than only aiming for the final answer.
One candidate's full-day on-site had six 45-minute interviews, with dissertation and project walk-throughs and follow-ups on methodology and statistical trade-offs. Another was asked to explain p-values to a clinician. Behavioral questions included strengths and weaknesses and STAR-style teamwork prompts.
