Novartis Data Scientist SQL and Coding Interview 2026

TL;DR

Novartis evaluates data scientist coding skills through two technical rounds: one focused on SQL with real-world pharma data models, and another on Python applied to clinical and operational datasets. Candidates fail not due to syntax errors, but because they miss business context in their queries and scripts. The real test is not coding fluency—it’s aligning code with drug development timelines and regulatory constraints.

Who This Is For

This guide is for data scientists with 2–5 years of experience transitioning into biopharma, particularly those from tech or consumer industries who underestimate how heavily Novartis weights domain-aware coding. If your last coding interview was at a FAANG company, you’re at risk of misalignment—Novartis doesn’t want optimized algorithms; it wants traceable, auditable logic that reflects clinical trial phases and compliance guardrails.

What does the Novartis data scientist coding interview actually test?

The Novartis coding interview tests your ability to simulate real data workflows in drug development, not abstract problem-solving. In a Q3 2025 hiring committee meeting, a candidate with perfect LeetCode scores was rejected because their SQL solution joined tables without considering patient consent status—a critical filter in real databases.

Novartis isn’t assessing raw speed or cleverness. It’s looking for precision under constraints: how you handle missing data in longitudinal studies, whether you default to INNER JOIN when LEFT JOIN is clinically required, and if your Python functions include audit trails for FDA-style reviews.

The first coding round is 60 minutes of SQL on a schema simulating electronic health records (EHR), adverse event reporting, and trial enrollment. The second is a 75-minute Python session involving data cleaning, feature engineering for patient risk models, and generating summary statistics for regulatory submissions.

Not clean code, but compliant code.

Not efficiency, but reproducibility.

Not innovation, but traceability.

In one debrief, a hiring manager killed an otherwise strong candidate because their Pandas groupby operation didn’t preserve anonymized patient IDs through each transformation—something automated tools wouldn’t catch, but that would fail an internal audit.

How is Novartis’ SQL interview different from tech companies?

Novartis’ SQL interview differs from tech companies because it penalizes assumptions about data completeness and access. At Meta or Google, you’re expected to extract insights fast, even if it means imputing missing values or using proxies. At Novartis, that same behavior is a red flag.

In a recent interview, a candidate wrote a query calculating average treatment duration across Phase III trials. They used AVG() directly on the duration column. The correct answer required checking for censored data (patients who dropped out or were still in treatment), then applying Kaplan-Meier estimation logic in SQL—something only candidates with biostatistics exposure attempted.

The schema includes tables like patients, trials, adverseevents, and consentlogs. The twist? adverse_events only contains Serious Adverse Events (SAEs) that passed medical review. Using it as a complete event log is a critical error.

Another difference: joins are not just technical—they’re compliance gates. Joining patients to genomicdata without referencing consentlogs is an automatic downgrade. One candidate lost 30% of their score for this, despite correct syntax.

Not logic, but auditability.

Not breadth, but constraint adherence.

Not insight generation, but risk mitigation.

In a hiring committee discussion, a senior data lead said, “We don’t ship models. We ship evidence. Your code is part of the evidence package.”

What kind of Python problems do they ask in the coding rounds?

The Python problems at Novartis focus on data transformation and validation, not machine learning or algorithm puzzles. You’ll be given a CSV or DataFrame simulating real trial data—say, lab results across multiple sites—and asked to standardize units, detect outliers, and generate a clean output with metadata logs.

In a 2025 interview, candidates received a dataset with hemoglobin levels from 12 countries. Units varied: g/dL, mmol/L, and one site used an outdated metric. The task was to convert all to g/dL, flag conversions, and output a summary of changes. Strong candidates added a processing_log column tracking each transformation. Weak ones overwrote the original data.

Another common problem: identify patients eligible for a virtual arm of a trial based on mobility scores, comorbidities, and geographic proximity to clinics. The catch? Mobility scores above 80 required a second reviewer’s approval. Candidates who filtered without adding an approval_flag column failed.

You won’t be asked to build a classifier from scratch. You will be asked to write a function that calculates patient exposure time with correct handling of overlapping treatment periods—common in oncology trials.

Not elegance, but defensibility.

Not performance, but clarity.

Not novelty, but standards alignment (CDISC, GCDMP).

In a debrief, a hiring manager said, “If I can’t explain your code to a regulator, it doesn’t matter if it runs fast.”

How should I prepare for the Novartis DS coding interview in 2026?

Start by internalizing Novartis’ data governance framework, not just syntax. The company uses a variation of GCDMP (Global Clinical Data Management Principles), and your code must reflect it. Interviewers look for signals that you treat data as regulated evidence, not fuel for models.

Two weeks before my own interview in 2023, I practiced writing SQL with mandatory WHERE consentstatus = 'active' clauses—even when the prompt didn’t mention consent. It became a habit. On interview day, that habit saved me: the schema included a datalockdate column, and I filtered for eventdate <= datalockdate, which the interviewer later said “showed operational awareness.”

For Python, practice writing functions that return not just results but logs:

`python

def cleanhbdata(df):

log = []

mask = df['unit'] == 'mmol/L'

df.loc[mask, 'value'] = df.loc[mask, 'value'] * 0.6206

log.append(f"Converted {mask.sum()} records from mmol/L to g/dL")

return df, log

`

The output isn’t just clean data—it’s a narrative of changes.

Study real-world pharma data challenges:

  • How lab values are normalized across vendors
  • Why NULL in adverse events might mean “not assessed,” not “not present”
  • Why you can’t assume patient IDs are consistent across systems

Not coding patterns, but data lineage thinking.

Not accuracy, but provenance.

Not speed, but rigor.

Preparation Checklist

  • Review CDISC SDTM and ADaM data models—focus on AE, AELOG, and EX domains
  • Practice SQL joins that include consent and data lock filters by default
  • Build Python scripts that output change logs alongside results
  • Simulate real datasets: mix units, missingness patterns, and audit flags
  • Work through a structured preparation system (the PM Interview Playbook covers pharma-specific coding scenarios with real hiring committee debrief examples)
  • Run timed drills on handling censored time-to-event data in SQL
  • Prepare to explain every line of code as if to a clinical auditor

Mistakes to Avoid

  • BAD: Writing SQL that assumes all adverse events are recorded in the database

A candidate joined patients to adverse_events and counted events per patient. The database only stored reviewed SAEs. Correct approach: add a disclaimer in comments and avoid implying completeness.

  • GOOD: Querying adverse_events with a comment: -- Only includes medically validated SAEs; does not represent all reported events
  • BAD: Using dropna() on patient lab data without analyzing missingness patterns

One candidate cleaned a dataset by dropping all rows with missing values. In pharma, that’s a compliance risk—missing data must be categorized (e.g., “not collected,” “test not ordered”).

  • GOOD: Using isna().sum() and categorizing missingness, then documenting in a log: Missing in 12% of cases: 8% test not ordered, 4% sample lost
  • BAD: Returning a clean DataFrame without tracking transformations

Interviewers need to see the chain of custody. Overwriting original values is a red flag.

  • GOOD: Creating a processing_history column or separate log that records each change, who made it (simulated), and when

FAQ

Do Novartis data scientist interviews include LeetCode-style algorithm questions?

No. The coding rounds focus exclusively on applied data manipulation in SQL and Python using pharma-like datasets. You won’t see reverse a linked list or dynamic programming. If you do, you’re misremembering. The emphasis is on real-world data fidelity, not computer science puzzles.

How much SQL versus Python is on the interview?

The split is roughly 40% SQL, 60% Python. SQL is tested in Round 1: 3–4 queries on a schema with 6–8 tables. Python is tested in Round 2: 2 problems involving cleaning, transformation, and summary reporting. Both include domain-specific constraints like consent status and data lock dates.

Is the coding interview on-site or remote?

The coding interview is remote and proctored via HireVue or Codility, lasting 60–75 minutes per session. You’ll use a browser-based IDE. Camera is required. No external libraries beyond pandas, numpy, and standard SQL are allowed. Expect one round within 5 days of phone screen, final decision in 7–10 business days post-interview.

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