Exl Data Scientist candidates describe a practical, technical process: project walkthroughs, ML and statistics fundamentals, Python and SQL, an applied business problem, and Gen AI questions such as RAG.
$93K
Avg. Base Comp
$115K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
Reports for the Exl Data Scientist role describe a practical interview that starts with your own work. In one account the first technical discussion covered the candidate's current project, including their role, model selection, feature engineering and evaluation metrics. Another candidate was asked to explain a project and how it related to the role at EXL. Be ready to defend your modeling choices, not just name them.
Fundamentals sit alongside the project talk. One candidate mentioned Python, SQL, statistics and machine learning questions, including a small Python frequency-counting problem. Another was asked what ML is, what AUC-ROC is and why accuracy can be a poor metric. NLP techniques also came up in one round.
One candidate described a later conversation built around a business problem: data cleaning, exploratory analysis, feature engineering, class imbalance, model selection and metrics. Another was asked about Gen AI, why RAG is needed and what a vector database is. A candidate interviewing for a Senior Data Scientist role, a different level, reported deeper questions on RAG, text-to-SQL and how XGBoost works, plus HR and manager rounds.
The closing conversation can turn personal. One candidate's final conversation covered reasons for switching, career plans, salary expectations and notice period. Another said they could not explain past career switches well. Prepare a clear story. Both Data Scientist candidates were still waiting for a reply, so outcomes are not well established.
Synthesized from 6 candidate reports by our editorial team.
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Real interview reports from people who went through the Exl process.
I interviewed with EXL for a Data Scientist role and experienced about three rounds. The first technical discussion covered my current project, including my role, model selection, feature engineering, and evaluation metrics. I was also asked questions on Python, SQL, statistics, and machine learning, including a small Python frequency-counting problem.
A later technical discussion focused on a practical business problem. We talked through data cleaning, exploratory analysis, feature engineering, class imbalance, model selection, and evaluation metrics. The final conversation covered my experience, projects, reason for switching, career plans, salary expectations, and notice period.
Overall, the process was more practical than theoretical. Be ready to explain the decisions behind your projects and to work through an applied data-science approach clearly.
Prep tip from this candidate
Prepare concise project walkthroughs and practice explaining Python, SQL, statistics, model choices, and evaluation metrics in the context of a business problem.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Exl
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Employee Salaries | |
| Bagging vs Boosting | |
| Size of Joins | |
| P-value to a Layman | |
| Type-ahead Search | |
| Three Zebras | |
| Precision and Recall | |
| Hurdles In Data Projects | |
| Target Indices | |
| Fine-Tuning VS RAG | |
| Transformer Encoder Layer | |
| Assumptions of Linear Regression | |
| Duplicate Rows | |
| RAG Strict Source Control | |
| Type I and II Errors | |
| Swap Variables | |
| Data Preparation for Imbalanced Data | |
| Overfit Avoidance | |
| SARIMA in Retail Forecasting | |
| Multicollinearity in Regression | |
| Credit Card Fraud Model | |
| Explaining Linear Regression to Different Audiences | |
| Random Forest from Scratch | |
| Google Earth Storage | |
| Your Strengths and Weaknesses | |
| Correlation in Regression | |
| Branch Sales Pivot | |
| Linear vs Logistic Regression | |
| Designing a Fraud Detection System |
Synthesized from candidate reports. Individual experiences may vary.
One candidate's first technical discussion covered their current project: their role, model selection, feature engineering and evaluation metrics. Another was asked to explain a project and its relevance to EXL, then asked what ML is, what AUC-ROC is, why accuracy can be a poor metric, and about NLP techniques.
One candidate reported questions on Python, SQL, statistics and machine learning, including a small Python frequency-counting problem. Expect to explain the reasoning behind your answers, since the same candidate described the process as more practical than theoretical.
A later technical discussion for one Data Scientist candidate focused on a practical business problem. They talked through data cleaning, exploratory analysis, feature engineering, class imbalance, model selection and evaluation metrics. Practice narrating an end-to-end approach and justifying each choice.
One candidate's second technical round asked why they wanted to join EXL, what Gen AI is, why RAG is needed, what a vector database is and why they should get the role. A Senior Data Scientist candidate separately reported RAG, text-to-SQL and XGBoost mechanics, so review these if your résumé includes LLM work.
One candidate's final conversation covered their experience and projects, reason for switching, career plans, salary expectations and notice period. Another said they struggled to explain past career switches. A Senior Data Scientist candidate described HR and manager rounds. Prepare a concise, honest career story.
