Amazon Data Scientist Interview Questions

1.Tell me about a time you analyzed customer data and discovered something that changed how your team approached serving them.

2.Tell me about something you learned in the last year that completely changed how you approach data science problems.

3.Describe a time when you saw a data quality or infrastructure issue that wasn't your responsibility, but you decided to fix it anyway.

4.Tell me about a complex data problem where you found a much simpler solution than what was originally proposed.

5.Walk me through a time you had to make a recommendation about a model or analysis when you only had partial data or uncertain assumptions.

6.Describe a time when you helped someone on your team develop their analytical or technical skills, and what the outcome was.

7.Write a function to calculate the median delivery time for Prime orders, given that we have billions of delivery records stored in a distributed system.

8.You're tasked with building a feature store for Alexa's machine learning models. What are the key technical requirements you'd need to consider?

9.Walk me through how you'd design an A/B test to measure the impact of a new ML model on Prime membership conversions, considering Amazon's existing experimentation platform.

10.You notice that your fraud detection model's precision dropped from 95% to 85% over the past month, but recall stayed the same. What's your investigation approach?

11.You're building a recommendation model for Amazon.com that needs to handle Black Friday traffic - 100x normal load with sub-50ms latency requirements. How would you design the inference pipeline?

12.How would you build a real-time anomaly detection system for AWS service metrics that needs to scale across millions of customer resources?

13.Design a data pipeline to support Amazon Go's computer vision models that process video from 1,000+ cameras across hundreds of stores. The system needs to handle 50TB of video data daily and provide real-time inference for checkout events.

14.You need to design a personalization system for Alexa that can serve recommendations to 100 million devices globally with under 100ms latency. Each user interaction generates new signals that should influence future recommendations.

15.Design the inventory forecasting system for Amazon.com that predicts demand for 500 million products across thousands of fulfillment centers. The system needs to handle flash sales and seasonal spikes while minimizing both stockouts and overstock costs.

16.Build a feature engineering platform for Prime Video's recommendation models that needs to process viewing behavior from 200 million subscribers and serve features for real-time inference during video browsing.

17.Design a fraud detection system for AWS that can analyze billions of API calls per day across all services and detect suspicious patterns in near real-time while minimizing false positives that would block legitimate customer workloads.

18.You're building the analytics platform that powers Amazon's supply chain optimization across 400+ fulfillment centers globally. The system needs to process warehouse sensor data, order flows, and transportation networks to make inventory positioning decisions worth billions in cost optimization.

19.Tell me about a time you had to push back on a stakeholder who wanted you to change your model or analysis methodology. How did you handle it?

20.Describe a situation where you took ownership of a data project or initiative that wasn't formally assigned to you. What motivated you to step up?

21.Walk me through a time when you had to influence a product manager or engineering team to adopt your analytical recommendations without having any direct authority over them.

22.Tell me about a time you had to make a quick decision about a model or analysis approach when you didn't have all the data you wanted. How did you proceed?

23.Describe a time when you discovered a significant problem with existing analytics or models that multiple teams were relying on. How did you handle the situation?

24.Walk me through a time when you had to convince your team or leadership to invest significantly more time and resources in a project because your initial analysis showed it would have much bigger impact than originally scoped.

25.Prime Day is coming up and we expect 10x normal traffic on Amazon.com. Estimate how many additional data scientists we'd need to monitor and optimize our recommendation systems during the event.

26.You're analyzing customer churn for Prime membership and notice that cancellation rates are 20% higher in certain zip codes. Walk me through how you'd investigate this pattern.

27.We're launching a new AWS service and need to price it competitively. Estimate the total addressable market for cloud-based machine learning inference services.

28.Alexa's voice recognition accuracy dropped 2% globally last week, but only for users speaking with non-American accents. How would you approach diagnosing and quantifying the business impact?

29.Amazon is considering entering a new international market. Estimate the revenue impact of launching Prime membership in a country with 50 million internet users where e-commerce adoption is currently 15%.

30.You discover that our product search algorithm is systematically ranking Amazon's private label products higher than equivalent third-party products, even when the third-party products have better reviews. How do you approach this situation?