Tiger Analytics Data Scientist interviews reported three to four rounds, combining Python, SQL and ML fundamentals with detailed, end-to-end discussion of past projects and an HR conversation.
$110K
Avg. Base Comp
$160K
Avg. Total Comp
3-4 rounds
Typical Rounds
2-4 weeks
Process Length
Tiger Analytics Data Scientist candidates report a structured process that ranges from three to four rounds in the available accounts. The recurring theme is a detailed defense of your own project work: interviewers may ask you to move from exploratory analysis and missing-data choices through feature engineering, model selection, evaluation, deployment, and post-launch drift monitoring. Be ready to explain why a decision was appropriate, what trade-off you made, and what happened when the work met a real constraint.
Technical screens reportedly cover practical foundations rather than only difficult algorithms. Candidates described Python data handling, loops, dictionaries, and string work; SQL joins, aggregations, second-highest salary, and above-average-salary queries; plus supervised versus unsupervised learning, bias-variance, classification metrics, and AUC-ROC. One account also named imbalanced data, Random Forest versus XGBoost versus LightGBM, and NLP/transformers.
A later technical or project-focused discussion may be CV-driven, so rehearse one or two projects end to end, including business benefit where you can substantiate it. HR was reported as a final conversation about communication, goals, fit, and in one case compensation. Evidence on exact sequencing varies, but the project depth and core technical coverage recur across reports.
Synthesized from 4 candidate reports by our editorial team.
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Featured question at Tiger Analytics
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Closest SAT Scores | |
| Top Three Salaries | |
| Experiment Validity | |
| Prime to N | |
| Top 3 Users | |
| Find the Missing Number | |
| Maximum Profit | |
| Retailer Data Warehouse | |
| Bagging vs Boosting | |
| Get Top N Frequent Words | |
| New Partner Card | |
| Minimum Absolute Distance | |
| Missing Housing Data | |
| Target Indices | |
| Median O(1) | |
| Assumptions of Linear Regression | |
| Digit Accumulator | |
| Transformer Encoder Layer | |
| Bias - Variance Tradeoff and Class Imbalance in Finance | |
| Matrix Rotation | |
| Count Transactions | |
| KNN From Scratch | |
| Possible Triangles | |
| Yelp-like System | |
| Production Model Monitoring | |
| Data Preparation for Imbalanced Data | |
| Finding the Maximum Number in a List | |
| String Palindromes | |
| Minimum Directional Path |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial technical conversation or coding screen covering practical Python data handling, loops, dictionaries, or string manipulation; SQL joins and aggregations; and machine-learning basics such as supervised versus unsupervised learning, evaluation metrics, and bias-variance.
Reports describe a deeper discussion of prior work, including preprocessing, feature engineering, model selection, evaluation, technical challenges, and scenario-based questions. Candidates may also encounter SQL tasks such as second-highest salary or identifying employees paid above an average.
One candidate described a project-focused discussion that moved from EDA, missing-value treatment, and imbalanced data to model comparisons, classification metrics, deployment details, and data or model drift. This depth may vary by interviewer and project.
Candidates report a final HR discussion focused on behavioral fit, communication, career goals, and team alignment; one account also included salary discussion. Prepare concise examples that connect your experience to the role.