Novartis analyst interviews reported by candidates combine SQL and database fundamentals, project deep-dives, case or written assessments, and HR rounds. Pharma data familiarity helped in one report.
$88K
Avg. Base Comp
$110K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describing Novartis analyst interviews point to SQL and database fundamentals as the most concrete technical theme. One candidate who was not selected after the first technical round was asked about primary versus foreign keys, how to find duplicate records, and the difference between DROP and TRUNCATE. The hardest question gave two small tables and asked for the row counts from left, right, inner, and full joins. That means reasoning about matched and unmatched rows, not just writing syntax. Another candidate who received an offer for an associate analyst role also reported detailed join questions, including cross joins, plus resume-based questions about past projects and internships.
The structure differs between reports. One candidate described a group discussion on AI topics, a case study, and a technical round. Another, for an associate analyst role, described a written statistics and probability assessment, a case study presented as non-elimination, technical rounds, guesstimation or puzzle questions, and an HR interview. A third, for a Real World Evidence role, described an HR screen, an aptitude test, a hiring-manager technical interview, a managerial round, and a final HR discussion. That last candidate said pharma datasets such as IQVIA, LAAD, and 867 Integrichain mattered more than advanced algorithms.
A practical plan is to rehearse join row-count exercises on small tables, practice explaining your projects and the database choices behind them, and review basic statistics and probability. Prepare simple answers on why Novartis and where you want to grow. If you are applying to a team that works with commercial or real-world data, learn the common pharma dataset terms. The reports come from different analyst roles, so confirm the format with your recruiter.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Novartis process.
I didn't make it past the first technical round, but the questions give a good sense of what this Novartis data analyst interview looks like. It started with a group discussion built around AI topics, so it's worth brushing up on current AI developments beforehand. The group discussion was followed by a case study round, and the same interviewer ran that, the group discussion, and my first technical round, so it felt like one continuous session.
The technical round opened with a self-introduction, then moved into database basics. I was asked the difference between a primary key and a foreign key, and how I would find duplicate records in a given table. The question that took the most thinking was a pair of tables where I had to apply left, right, inner, and full joins and work out the number of rows each one returned. That required reasoning about matched and unmatched rows, not just writing the syntax. The round also covered a quick conceptual question on what a class and an object are, a discussion of my projects, and the difference between DROP and TRUNCATE.
The process typically includes two technical rounds and a final HR round. The HR questions were fairly general: why Novartis, what the role involves day to day, and where I see myself in five years. The stack appears to vary by team. The rounds I went through centered on SQL, but Python and C++ coding questions at medium LeetCode difficulty, along with Excel, SQL, and pandas topics, have also come up for this role. If you're preparing, be ready for both conceptual database questions and hands-on query reasoning, and don't assume the technical focus will match a specific language.
Prep tip from this candidate
Practice predicting the row count for left, right, inner, and full joins on small sample tables until you can reason through unmatched rows quickly. Also be ready to explain how to find duplicate records and the difference between DROP and TRUNCATE.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Novartis
What do you tell an interviewer when they ask you what your strengths and weaknesses are?
| Question | |
|---|---|
| 2nd Highest Salary | |
| Monthly Customer Report | |
| Experiment Validity | |
| Last Transaction | |
| Weighted Keys | |
| Hurdles In Data Projects | |
| Cumulative Distribution | |
| Brain Cancer Treatment Outcomes | |
| Total Spent on Products | |
| P-value to a Layman | |
| Reducing Error Margin | |
| Detecting ECG Tachycardia Runs | |
| Size of Joins | |
| Fair Coin | |
| Causal Email Journey | |
| Greatest Common Denominator | |
| Random Forest Explanation | |
| Subscription Retention | |
| Always Excited Users | |
| Retailer Data Warehouse | |
| Secret Wins | |
| Missing Housing Data | |
| Flatten JSON | |
| Valid Anagram | |
| Xgboost vs Random Forest | |
| 85% vs 82% | |
| Cumulative Reset | |
| Rider Discount | |
| Digit Accumulator |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports an initial HR screening covering background, motivation, and salary expectations. Other candidates describe HR conversations later in the process, so the timing of HR contact may vary by team and role.
Candidates report an early assessment in some processes. One associate analyst candidate describes a written test on statistics and probability, and a Real World Evidence candidate describes an aptitude test on logical reasoning and quantitative skills. Not every report mentions one.
A case study appears in two reports, and one associate analyst candidate says it was presented as non-elimination. One candidate also describes an AI-themed group discussion before the case study. Prepare to structure an ambiguous problem and explain your reasoning aloud.
Candidates report SQL-heavy technical questions: key types, duplicate detection, DROP versus TRUNCATE, and join row counts. Interviewers also probed past projects and database experience. One report says the stack varies by team, so Python, Excel, or pandas may also come up.
Later rounds mix behavioral and fit questions. Candidates report questions about why Novartis, what the role involves, career goals, curiosity, and teamwork or problem-solving style. A Real World Evidence candidate also describes a separate managerial round before the final HR discussion.
