Exl's Data Analyst interviews mix an SQL/aptitude screening with SQL, Python, and stats technical rounds, case studies or brainteasers, deep resume walkthroughs, and a behavioral or HR round; round counts vary by candidate.
$80K
Avg. Base Comp
$95K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Exl's Data Analyst interviews typically start with a screening step—either an aptitude test or a multiple-choice quiz—that checks SQL joins, Python basics, and sometimes NLP fundamentals like tokenization and lemmatization before any live conversation happens. From there, candidates report technical rounds that lean hardest on SQL window functions, join behavior, and Pandas dataframe work. Several interviewers also probe statistics and modeling concepts such as the Gauss-Markov assumptions, Bayes' theorem, logistic regression and sigmoid math, regularization, or basic XGBoost ideas. The mix of SQL versus stats and modeling content varies from one account to the next, so it pays to prepare both.
Beyond coding, Exl interviewers spend real time on case-style reasoning and brainteasers. One candidate worked through three case studies, while another counted the squares on a chessboard and removed duplicates from a Python list in a notably relaxed, conversational panel. Resume and project walkthroughs show up in nearly every account. Interviewers ask candidates to defend model accuracy numbers, explain their approach, and discuss internship work in depth, so a clear narrative about your own projects matters as much as syntax recall. One candidate was asked whether 80% accuracy counted as success and how they would improve the model, which is a useful prompt to rehearse against your own work. HR or behavioral rounds close out most processes and cover fit, motivation, situation-based scenarios, and sometimes informal questions about hobbies or family.
Synthesized from 11 candidate reports by our editorial team.
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| Question | |
|---|---|
| Employee Salaries | |
| Bagging vs Boosting | |
| Size of Joins | |
| P-value to a Layman | |
| Type-ahead Search | |
| Three Zebras | |
| Hurdles In Data Projects | |
| Target Indices | |
| Assumptions of Linear Regression | |
| Duplicate Rows | |
| RAG Strict Source Control | |
| Type I and II Errors | |
| Swap Variables | |
| Data Preparation for Imbalanced Data | |
| Overfit Avoidance | |
| Multicollinearity in Regression | |
| Credit Card Fraud Model | |
| Explaining Linear Regression to Different Audiences | |
| Random Forest from Scratch | |
| Google Earth Storage | |
| Your Strengths and Weaknesses | |
| Branch Sales Pivot | |
| Correlation in Regression | |
| Linear vs Logistic Regression | |
| Scaling Up Recommender | |
| Designing a Fraud Detection System | |
| Random SQL Sample | |
| Booking Regression | |
| Lasso vs Ridge |
Synthesized from candidate reports. Individual experiences may vary.
Several candidates report an initial screening test before any live interview. Reported formats range from a 46-question, 60-minute MCQ covering NLP basics, SQL joins, and Python output tracing, to a 40-question aptitude test split across English, reasoning, and quantitative sections. This stage typically checks foundational breadth rather than depth, rewarding speed and accuracy on fundamentals like join types and basic Python data structures.
Candidates consistently describe one or two technical rounds centered on SQL—joins, UNION vs UNION ALL, window functions, and lag/lead queries—often paired with Python or Pandas dataframe exercises. Several accounts add statistics or modeling questions on top of SQL, including logistic regression and sigmoid math, regularization, Gauss-Markov assumptions, Bayes' theorem, or basic XGBoost concepts. The mix of SQL versus stats/ML content varies across reports.
Some candidates describe case studies or puzzle-style questions rather than pure algorithm problems. One went through three case studies testing how they framed assumptions, while another solved a chessboard-counting brainteaser and a list-deduplication problem in a relaxed, conversational panel. Interviewers in these rounds reportedly care about how a candidate reasons through a problem out loud.
Nearly every candidate was asked to explain their own projects or internship work in detail, with interviewers pushing on model accuracy and how a result could be improved, not just what was built. This typically pairs with a separate HR or behavioral round covering motivation, team fit, and in some cases informal questions about hobbies or family. A final round with a hiring manager may combine project discussion, SQL follow-ups, and fit questions.
