Flatiron Health Data Scientist SQL and Coding Interview 2026
TL;DR
Flatiron Health’s data scientist interviews test applied SQL and Python in oncology-adjacent contexts, not abstract algorithms. Candidates fail not from syntax errors but from misaligned scope—writing code for a generic dataset instead of a clinical workflow. The process spans 3 weeks: 1 screening, 1 take-home, 1 onsite with 2 coding rounds and 1 behavioral. Success hinges on precision in window functions and data model assumptions, not flashy pandas tricks.
Who This Is For
This is for data scientists with 2–5 years of experience transitioning from tech or health tech into specialized oncology analytics roles at Flatiron. You’ve written SQL daily and used Python for ETL or analysis, but you haven’t worked with EMR-derived data at scale. You’re targeting mid-level roles—$140K–$165K base salary—and need to prove you can ship clean, auditable code in a regulated environment.
What does the Flatiron Health data scientist coding interview actually test?
Flatiron doesn’t assess computer science fundamentals. They test whether you can write SQL that survives peer review in a clinical validation setting. In a Q3 2024 debrief, the hiring manager rejected a candidate who used recursive CTEs in PostgreSQL—technically correct, but unreviewable by non-engineers.
The real skill isn’t recursion or self-joins. It’s defensive coding: aliasing every column, avoiding SELECT *, and commenting aggregations that could impact patient counts. One candidate passed because their code included:
-- Assumption: One primary cancer per patient per episode, per Flatiron v3 schema
That line signaled awareness of data model constraints. It wasn’t in the prompt, but it surfaced in the debrief as evidence of judgment.
Not speed, but auditability. Not complexity, but traceability. Not elegance, but reproducibility.
Interviewers will ask you to calculate time between treatment lines. You’ll need to use LAG() with explicit NULL handling, not just ORDER BY date. They’re not testing your knowledge of window frames—they’re testing whether you assume gaps in treatment mean gaps in data. In oncology data, a 60-day gap doesn’t mean no care occurred; it means the record wasn’t ingested. Your code must reflect that uncertainty.
One candidate failed because they filtered out patients with <3 visits. The dataset had sparse documentation. The interviewer noted: “This assumption wasn’t stated. In real work, that would bias survival curves.” The problem wasn’t the filter—it was the silence around it.
How is Flatiron’s SQL bar different from other health tech companies?
Flatiron’s SQL standard is tighter than Oscar Health’s and more clinically grounded than Tempus’. At Oscar, you might get away with a naked GROUP BY if results are correct. At Flatiron, if you aggregate without specifying how ties are broken in treatment dates, you’ll be challenged.
In a 2023 hiring committee meeting, two candidates produced identical results on a progression-free survival calculation. One used ROW_NUMBER() with a secondary sort on provider ID. The other used RANK() and accepted duplicates. The first passed; the second didn’t. The debrief note read: “Cannot have duplicate rows in analysis datasets—this introduces non-reproducible sampling.”
Not correctness, but determinism. Not output, but provenance. Not query brevity, but decision transparency.
Flatiron’s schema is wide and denormalized—patients, treatments, labs, imaging, pathology—all flattened into semi-immutable tables. You’re not joining 8 tables. You’re filtering one massive table with care. A senior data scientist told me: “We care more about your WHERE clause than your JOINs.”
Example: when filtering for active patients, you can’t just use “last visit > 6 months ago.” You must account for flat lines in cancer trajectories. One candidate wrote:
WHERE lasttreatmentdate >= DATEADD(month, -6, GETDATE())
OR (cancertype IN ('AML', 'ALL') AND lastlab_date >= DATEADD(month, -3, GETDATE()))
That conditional logic—specific to acute vs. chronic cancers—was called out in the debrief as “domain-aware.” It wasn’t required, but it showed calibration.
What kind of coding take-home should I expect?
The take-home is a 48-hour Python and SQL task involving synthetic oncology data. You’ll receive a schema diagram, 3 CSVs (patients, treatments, adverse events), and 4 prompts. One is always: “Identify patients who initiated second-line therapy within 30 days of first-line discontinuation.”
Candidates underestimate the documentation requirement. You must submit a 1-page README explaining assumptions. In a Q2 2025 debrief, a candidate was rejected despite correct code because their README said “assumed clean data.” The dataset had missing treatment_end dates—exactly the kind of gap Flatiron analysts must handle.
Not code correctness, but assumption surfacing. Not functionality, but boundary specification. Not automation, but judgment signaling.
Your Python code must be PEP8-compliant and use pandas, not Polars or DuckDB. One candidate used .pipe() extensively for modularity. The reviewer wrote: “Hard to trace intermediate states—avoid functional constructs in clinical reports.” Flatiron’s codebase prioritizes line-by-line auditability over elegance.
You’ll be asked to output to CSV and include summary statistics in the README. One prompt always asks: “How many patients were excluded, and why?” This isn’t a coding question—it’s a data governance test.
Top performers include a section like:
- Excluded 120 patients: no recorded treatment start date
- Excluded 42 patients: first-line therapy duration < 7 days (potential data entry error)
This mirrors how Flatiron’s real analysts document exclusion criteria for regulatory submissions.
How should I prepare for the onsite coding rounds?
The onsite has two 45-minute coding sessions: one pure SQL, one Python + SQL hybrid. The SQL round is live in a browser-based editor (no autocomplete). You’ll be given a schema and asked to write queries on the fly.
One common prompt: “For patients on drug X, calculate the median time to adverse event Y, stratified by age group.” The trap? Adverse events may precede treatment start. A candidate who didn’t filter for aedate >= treatmentstart_date was marked “lacks clinical data intuition.”
Not query syntax, but temporal logic. Not aggregation, but causality framing. Not filtering, but sequence validation.
The Python round gives you a Jupyter notebook with a pandas DataFrame already loaded. You’re asked to clean, aggregate, and visualize. But the visualization is a red herring—no one cares about your matplotlib style. They care whether you handle missingness intentionally.
Example: a candidate imputed missing BMI values with median. The interviewer stopped them: “Would you do that in a real report?” The correct response: “No—BMI is clinically significant. I’d flag it as missing and analyze non-missing subset with sensitivity analysis.”
In a 2024 debrief, a hiring manager said: “I don’t want a data scientist who ‘completes’ tasks. I want one who pauses when data quality is ambiguous.”
You’ll be interrupted mid-code. Interviewers will say: “What if the treatment dates are in Eastern time but lab draws are in UTC?” Your response must show schema-level awareness, not just coding fixes.
Preparation Checklist
- Study Flatiron’s published data model diagrams—especially treatmentline and episodeof_care tables
- Practice window functions with LAG/LEAD and explicit NULL handling in date sequences
- Write SQL with full table aliasing and commented assumptions on every aggregation
- Rebuild 2 take-home projects using only pandas and vanilla matplotlib—no seaborn or plotly
- Work through a structured preparation system (the PM Interview Playbook covers oncology data modeling with real Flatiron-style schema examples)
- Simulate 48-hour take-home constraints: 6 hours total work time, then stop
- Review FDA guidance on real-world evidence—know terms like “data provenance” and “audit trail”
Mistakes to Avoid
- BAD: Writing SQL that works but doesn’t state assumptions
Candidate writes:
SELECT patientid, MAX(treatmentdate)
FROM treatments
GROUP BY patient_id
No comment on duplicates or ties. Fails.
- GOOD: Same query, but adds:
-- Breaks ties by provider_id ascending, assuming earliest-entered record is primary
SELECT patientid, MAX(treatmentdate),
FIRSTVALUE(providerid) OVER (PARTITION BY patientid ORDER BY treatmentdate DESC, provider_id ASC)
FROM treatments
GROUP BY patient_id
This surfaces decision logic. Passes.
- BAD: Using .fillna() without justification in Python
Candidate runs df['bmi'].fillna(df['bmi'].median()) and moves on. Interviewer stops them.
- GOOD: Candidate writes:
mask = df['bmi'].notna()
subset = df[mask]
This shows intentionality. Passes.
- BAD: Treating Flatiron like a startup coding bar
Candidate uses CTEs for every subquery, recursive logic for patient episodes.
- GOOD: Flattens logic into sequential temp tables with clear names:
first_line = ...
firstlineclean = firstline[firstline.duration_days >= 7]
This matches Flatiron’s internal style. Passes.
FAQ
What level of SQL mastery is expected?
You must know window functions, CTEs, and execution order cold. But mastery isn’t about complexity—it’s about writing queries that non-engineers can verify. If your SQL requires a whiteboard explanation, it’s too dense. The bar is set by Flatiron’s internal SQL review checklist: every aggregation must have a tie-breaking rule, every filter must have a rationale.
Do I need oncology domain knowledge?
No formal knowledge is required, but you must reason about clinical timelines. You won’t be asked what FOLFOX is, but you will be expected to know that adjuvant therapy follows surgery, not precedes it. The interview tests whether you can infer plausible sequences from sparse data. Not memorization, but logic under uncertainty.
Is the take-home graded on code style?
Yes. Indentation, variable names, and comment density are evaluated. One candidate lost points for using df1, df2, dfclean. Flatiron’s codebase uses descriptive names like baselinecohort and postexclusionset. Your PEP8 compliance and naming consistency are proxies for long-term maintainability.
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