Eli Lilly Data Scientist SQL and Coding Interview 2026
TL;DR
Eli Lilly’s 2026 data scientist coding interviews prioritize real-world SQL efficiency over algorithmic trivia.
Candidates fail not from syntax errors, but from unclear decision logic under ambiguity.
The bar isn’t technical perfection—it’s structured thinking with therapeutic domain awareness.
Who This Is For
This is for data scientists with 2–5 years of industry experience applying to mid-level roles at Eli Lilly in 2026, particularly those transitioning from non-pharma tech roles. If your background is in SQL-heavy analytics but lacks exposure to clinical or real-world evidence data structures, you’re in the bullseye of the evaluation gap. Hiring managers assume technical baseline competence; they’re testing judgment refinement, not whether you can write a window function.
What does the Eli Lilly data scientist coding interview actually test in 2026?
Eli Lilly’s data scientist coding interview tests how you handle incomplete specifications, not your ability to regurgitate LeetCode patterns.
In a Q3 2025 debrief, a candidate solved a time-to-event SQL problem flawlessly—yet was rejected because they didn’t ask whether “event start” should be anchored to first dose or consent date. The hiring manager stated: “They treated the schema like a sandbox, not a clinical trial.”
Technical accuracy is table stakes. The real evaluation layer is assumption articulation.
Interviewers aren’t measuring speed. They’re measuring whether your first question is “What’s the business context?” rather than “Can I see the schema?”
This reflects a broader shift: since 2023, 78% of rejected candidates had clean code but zero therapeutic logic signaling.
Not coding speed, but diagnostic clarity.
Not query correctness, but context calibration.
Not feature engineering creativity, but constraint acknowledgment.
During one onsite, a candidate was given a task involving patient dropout rates across trial phases. They built a perfect cohort retention waterfall in SQL—but assumed dropout meant “no follow-up visit,” when in pharma, dropout is protocol-defined (e.g., missed two visits + no re-engagement in 30 days). The feedback was: “They applied a tech-industry heuristic to a regulated medical process.” That’s fatal.
How is the SQL section structured in the Eli Lilly data science interview?
The SQL round is a 60-minute live coding session using a simulated EHR and clinical trial database, typically via HackerRank or a custom internal platform.
It includes two problems: one exploratory (e.g., “Find patterns in adverse event reporting across regions”) and one outcome-linked (e.g., “Calculate time from diagnosis to first prescription, adjusting for gaps”).
The schema includes tables like PATIENTS, ENCOUNTERS, MEDICATIONLOG, AEREPORTS, and TRIAL_ENROLLMENT—normalized but with intentional ambiguities (e.g., multiple dose columns, no primary key documentation).
Candidates are expected to identify and resolve referential integrity issues on the fly.
In a January 2026 mock interview, a candidate spent 15 minutes normalizing the MEDICATION_LOG table by stitching fragmented dose entries before writing the main query. The interviewer noted: “They treated data quality as part of the solution, not a side comment.” That became a key positive signal.
You are not being tested on whether you know RANK() vs DENSE_RANK().
You are being tested on whether you validate time zones in timestamp fields before joining across tables.
One candidate lost points for calculating “days between visits” using date truncation without checking whether visitstartdatetime included time precision. It did. The error created 1-day skews in 12% of rows.
Not schema navigation, but anomaly anticipation.
Not join syntax, but temporal alignment discipline.
Not aggregation accuracy, but bias awareness in missingness patterns.
The scoring rubric weighs three dimensions equally: correctness (40%), clarity of comments and intermediate steps (30%), and explicit handling of edge cases (30%). A correct query with no comments scores lower than a 90%-correct query that documents assumptions.
Is Python required, and how deep does the coding bar go?
Yes, Python is required, but not for algorithm challenges.
The coding assessment focuses on data wrangling with pandas, merging structured EHR extracts, and generating summary statistics with scientific libraries (numpy, scipy).
You will not be asked to implement a binary tree. You will be asked to calculate confidence intervals for adverse event rates across subpopulations and flag statistically unstable estimates.
In a 2025 debrief, a hiring manager rejected a candidate who used bootstrapping for CI estimation but didn’t justify why it was better than Clopper-Pearson for rare events. “They reached for complexity without diagnostic intent,” the manager said. Simpler, justified methods score higher than technically advanced, unexplained ones.
You must know how to:
- Reshape longitudinal patient data from wide to long format
- Handle overlapping medication periods with coarser time precision
- Impute lab values using last-observation-carried-forward—but only after stating the bias implications
The coding bar is depth in domain-appropriate technique, not breadth of computer science knowledge.
A senior data scientist on the diabetes therapeutics team once said: “If you use groupby().apply() when vectorized operations would work, I assume you don’t care about production runtimes.”
Not coding elegance, but operational pragmatism.
Not library fluency, but method justification.
Not execution speed, but scalability awareness.
One candidate passed by writing a 20-line pandas script with five commented warnings about missing data patterns. Another failed with a 5-line vectorized version that ignored zero-values in continuous lab measures. The difference wasn’t code length—it was risk signaling.
How do Eli Lilly interviewers evaluate coding solutions differently from tech companies?
Eli Lilly evaluates coding solutions through a clinical data governance lens, not a pure engineering efficiency frame.
Where Google might dock points for not using a hash map, Eli Lilly docks points for not documenting how a join could introduce duplicate patient records.
In a November 2025 debrief for a candidate who joined the Oncology DS team, the HC noted: “They added a HAVING COUNT(DISTINCT trial_id) = 1 clause when filtering patients—prevented accidental cross-trial contamination. That’s the kind of defensive coding we want.”
Tech companies optimize for scalability and elegance.
Eli Lilly optimizes for auditability and traceability.
This shows up in expectations:
- Every derived variable must be traceable to source fields
- Time-based logic must specify timezone handling
- Missing data assumptions must be stated explicitly
One candidate failed because they calculated “days supply” by dividing total tablets by daily dose—without checking whether the prescription included tapering instructions. In real data, dose changes mid-script. The interviewer wrote: “Assumed linearity in a non-linear process.”
Not correctness in isolation, but defensibility in review.
Not performance optimization, but regulatory foresight.
Not code brevity, but error surface minimization.
An interviewer from the Real World Data team once said: “We’re not shipping features. We’re generating evidence. The code is part of the regulatory record.” That mindset shift is non-negotiable.
Preparation Checklist
- Build fluency with time-to-event calculations in SQL, especially with gaps and re-initiations
- Practice writing SQL that includes data quality checks as part of the query (e.g., row count pre/post joins)
- Simulate ambiguity by solving problems without full schema documentation
- Develop a standard comment template for assumptions, edge cases, and limitations
- Work through a structured preparation system (the PM Interview Playbook covers therapeutic-area-specific SQL cases with real debrief examples from pharma hiring committees)
- Review common EHR and claims data structures (e.g., OMOP, Sentinel)
- Run timed drills using synthetic clinical datasets with missingness and encoding inconsistencies
Mistakes to Avoid
- BAD: Writing a SQL query that joins PATIENTS to MEDICATION_LOG without checking for multiple rows per patient-visit.
- GOOD: Adding a pre-check with COUNT(*) vs COUNT(DISTINCT patient_id) and commenting: “Verifying one med row per visit; if >1, will need to aggregate or flag.”
- BAD: Calculating persistence as “days between first and last fill” without defining what counts as a refill gap.
- GOOD: Stating upfront: “Assuming 14-day grace period for refill; beyond that, treatment is considered discontinued,” then coding accordingly.
- BAD: Using pandas .dropna() without documenting the percentage of data removed and potential selection bias.
- GOOD: Printing missingness rates per variable and adding: “Dropped 18% of records due to missing baseline HbA1c. Sensitivity analysis recommended.”
FAQ
Do I need to know advanced statistics for the coding interview?
No—but you must know when and why to apply basic statistical methods. Interviewers reject candidates who calculate means without considering skew or missingness. Knowing to use median for length-of-stay or geometric mean for lab ratios is expected. Advanced stats are discussed in modeling rounds, not coding.
Is the coding interview done on paper, live, or take-home?
It’s a live 60-minute session via Zoom with a shared coding environment. No take-homes. No whiteboard. Expect interruptions: interviewers will change requirements mid-query to test adaptability. One candidate was told to “now exclude patients who died” halfway through a persistence analysis—how they adjusted mattered more than completion.
How important is knowledge of Eli Lilly’s therapeutic areas?
Critical. In 2025, three candidates failed the same SQL problem on time to first dose because they didn’t know that in diabetes trials, “diagnosis date” is often backfilled from lab values, not EHR diagnosis codes. Domain ignorance is interpreted as lack of preparation, not neutrality.
Ready to build a real interview prep system?
Get the full PM Interview Prep System →
The book is also available on Amazon Kindle.