sardine Data Scientist Interview: Questions & Prep (2026)

Sardine is a risk intelligence platform helping payment companies, neobanks, crypto exchanges, and banks stop fraud and meet AML compliance requirements. The Data Science team builds the core product: real-time transaction risk scores, device and behavioral fingerprinting models, and graph-based detection of money mules and fraud rings. This is applied ML in a high-stakes, adversarial environment, and interviews reflect exactly that.

Sardine currently has 35 open roles, signaling active hiring. The Data Scientist role typically sits at the intersection of ML engineering and product thinking. Candidates report being asked to reason through end-to-end system design as well as model evaluation under real-world constraints.

Across India, knok jobradar tracked 937 Data Scientist openings as of July 2026. City-level demand:

Salary bands for Data Scientists in India:

1. Sardine processes millions of transactions in real time. How would you design a fraud scoring model that works at low latency without sacrificing meaningful accuracy?

1. Fraud datasets are extremely imbalanced. What techniques do you use to handle class imbalance and how do you choose between them?

1. A merchant suddenly generates far more transactions than their normal volume late at night. Walk through how your anomaly detection system would flag and respond to this.

1. Ground truth in fraud is delayed. A transaction might only be disputed weeks or months after it happens. How do you handle model training and evaluation with this label delay?

1. How would you build a behavioral biometrics feature set to detect account takeover (ATO) fraud?

1. Sardine uses device fingerprinting as a core signal. How would you model the relationships between devices, accounts, and transactions to surface fraud rings using graph-based methods?

1. A fraudster is reverse-engineering your rules and coaching users to evade them. How do you build a model that stays resilient against adversarial adaptation?

1. You deploy a new model and precision drops sharply after two weeks. What are the first things you check?

1. How would you design a system to detect AML structuring (smurfing) patterns across thousands of accounts?

1. A compliance officer needs to explain a model rejection to a regulator. How do you make your fraud model interpretable without sacrificing too much accuracy?

1. How do you evaluate the business impact of your fraud model beyond standard ML metrics like AUC-ROC?

1. Describe a trade-off you made between fraud recall (catching more fraud) and false positive rate (blocking good customers). How did you decide?

Q: How do you handle class imbalance in a fraud detection dataset?

*Situation:* At a previous role, I was building a first-party fraud detection model for a digital lending product. Confirmed fraud cases were a very small fraction of all applications.

*Task:* Build a model that catches a meaningful share of fraud without flooding the operations team with false positives they cannot realistically review.

*Action:* I tested three approaches side by side: SMOTE oversampling on the minority class, majority-class undersampling, and adjusting class weights in the gradient boosting model. I also shifted our primary evaluation metric from accuracy to precision-recall AUC, which is far more informative on imbalanced data. After cross-validation, class-weight adjustment performed best because it preserved the real data distribution while still penalizing missed fraud more heavily than missed good users.

*Result:* The model outperformed the rule-based system it replaced on a holdout set, and false positive volume dropped to a level the operations team could handle within daily review queues.

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Q: A model you deployed starts degrading in production. What do you do?

*Situation:* After deploying a card fraud model at a fintech, I noticed precision dropping week over week while recall held steady. Something in the data had clearly shifted.

*Task:* Diagnose the root cause and stabilize performance without taking the model offline, which would expose the business to unchecked fraud risk.

*Action:* I first checked for data pipeline issues, null rates, and schema drift in incoming features. Finding no data quality problems, I ran a feature distribution comparison between training data and recent live data. Several device-related features showed significant drift. I also found that a large merchant had changed their checkout flow, shifting feature distributions for that segment specifically. I retrained on a rolling window of recent data and added monitoring alerts for feature drift above a defined threshold.

*Result:* Precision recovered within the next evaluation window, and the new monitoring setup caught further drift events automatically before they caused visible degradation.

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Q: How would you explain a model rejection to a non-technical compliance officer?

*Situation:* Our fraud model flagged and blocked a transaction for a high-value customer at a payments company. The compliance team needed to document the rejection for a regulator audit.

*Task:* Translate the model's decision into plain, auditable reasoning without revealing proprietary feature weights or triggering further questions about model opacity.

*Action:* I used SHAP values to identify the features driving the decision for that specific transaction. I then wrote a brief summary in plain language: the transaction showed a device not previously seen on this account, a shipping address inconsistent with the billing country, and a velocity pattern unusual for accounts of this tenure. I avoided model jargon entirely and framed each factor as a specific, observable event any compliance officer could understand.

*Result:* The compliance officer signed off on the documentation without further questions, and the format became a template the team used for all subsequent regulatory queries.

For fraud system design questions: Start by stating the business constraint (latency, precision vs. recall trade-off, label delay). Then walk through data sources, feature engineering, model selection, evaluation strategy, and deployment concerns in that order. Sardine interviewers are looking for end-to-end thinking, not just model selection.

For 'how would you handle X' questions: Use a structure of: problem definition, data, features, model, evaluation metric, and monitoring. Always explain why the metric you chose matches the business goal. For fraud, this usually means justifying why you prioritize recall or precision for a given context and the cost implications of each error type.

For trade-off questions: Name both sides of the trade-off explicitly before taking a position. State which side you would optimize for in a given context and the business reason behind that choice. Fraud contexts typically favor recall when fraud losses are high and precision when false positives erode customer trust or carry regulatory consequences.

For behavioral and past-experience questions: Use STAR (Situation, Task, Action, Result). Keep Situation and Task brief. Spend most time on Action (the specific choices you made and why you chose them over alternatives) and Result (a concrete outcome or a clear learning). Sardine interviewers typically follow up with 'why did you choose that approach over alternatives?' so be ready to defend your decisions with reasoning, not just outcomes.

For model interpretability questions: Know SHAP at a practical level. Be ready to discuss when to use global explanations (feature importance for governance and compliance audits) vs. local explanations (per-decision audit trails for individual transaction reviews).

Fraud domain depth. Generic ML answers will not land well. Sardine builds fraud and AML products, so interviewers want to see that you understand adversarial data environments, delayed labels, and the asymmetric cost of false positives vs. false negatives in a regulated context.

Production ML mindset. Candidates report that Sardine interviewers probe well beyond notebook-level thinking. They want to know how you handle feature drift, monitor model health in production, and retrain safely when the fraud landscape shifts against your current model.

Cross-functional communication. Data Scientists at Sardine work with compliance, operations, and product teams. Interviewers look for candidates who can explain a model decision to someone who has never encountered AUC or SHAP in their work.

Intellectual honesty. When a model underperforms or a feature idea does not work, interviewers want to see clear reasoning about why, not defensive justification. Framing failures as learning ('we tested this approach and here is what we found') lands better than claiming everything worked perfectly.

Speed with correctness. Real-time fraud scoring means ML under tight latency constraints. Be prepared to discuss feature computation strategies, model size trade-offs, and the practical limits of complex ensembles when latency matters as much as accuracy.

1. Know Sardine's product. Read their public blog and technical content about fraud detection, device intelligence, and AML before any interview round. Understanding their product philosophy shapes how you frame your answers.

1. Refresh fraud-specific ML concepts. Focus on: handling class imbalance (SMOTE, class weights, threshold tuning), evaluation metrics for imbalanced data (precision-recall AUC, F-beta score), and label delay strategies (positive-unlabeled learning, calibration on delayed feedback).

1. Study graph-based fraud detection. Sardine's device and behavioral fingerprinting relies heavily on graph methods. Review basics of graph neural networks and community detection algorithms. You do not need to have built a production graph system, but you should be able to discuss the approach, its trade-offs, and when you would choose it over tabular methods.

1. Practice system design. Pick two or three fraud scenarios (account takeover, payment fraud, money muling) and practice designing an end-to-end ML system for each. Walk through data, features, model choice, evaluation metric, and monitoring in sequence.

1. Prepare STAR stories. Have several specific stories ready: a model you built from scratch, a production failure you diagnosed, a trade-off decision you made, and a time you explained a technical concept to a non-technical audience.

1. Review interpretability tools. Be comfortable with SHAP at a practical level. Know when to use TreeExplainer vs. KernelExplainer and how to present outputs to compliance or operations stakeholders in plain language.

1. Prepare thoughtful questions. Asking how Sardine measures false positive impact on customer experience, or how they retrain models as fraud patterns evolve, signals domain curiosity and genuine interest in the product.

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Treating fraud detection like a standard classification problem. Fraud data is adversarial, delayed, and heavily imbalanced. Answers that ignore these constraints signal limited domain experience to Sardine interviewers who live with these challenges daily.

Optimizing for the wrong metric. Saying you would maximize accuracy on an imbalanced fraud dataset is a red flag. Always anchor your metric choice to the business cost of false positives vs. false negatives in the specific context you are describing.

Skipping the monitoring story. Interviewers want to know what happens after deployment. If your answer ends at 'and then we deployed the model,' you are leaving out the part Sardine cares about most: how you keep the model working as fraud patterns change.

Over-engineering the design. Proposing a complex high-feature-count deep learning ensemble when a well-tuned gradient boosting model with a handful of interpretable features would perform comparably and deploy at low latency shows poor practical judgment. Sardine values engineers who ship, not engineers who theorize.

Vague STAR answers. 'We improved fraud detection' is not a result. Name a specific decision you made, a method you chose, and what changed because of it. Concrete details make behavioral answers memorable and credible to interviewers evaluating dozens of candidates.

Not asking questions. Sardine is a product company with a specific technical thesis on fraud. Asking thoughtful questions about their approach signals domain curiosity and product awareness, and shows you have done your homework before the call.