Data Engineer II, HWEng Data Science & Analytics team

Amazon

You are given an array of servers represented by their processing power. You want to join consecutive servers together to form larger, more powerful servers. You can only join server i with server i+1 if the processing power of server i+1 is strictly greater than the processing power of server i. This joining process can cascade, meaning that if you join i and i+1, and the new combined processing power is less than i+2, you can then join the combined server with i+2, and so on. The goal is to maximize the number of these larger servers. For example: Consider the array [1, 2, 3, 4, 5]. You can join 1 and 2 to form 3. Then you can join 3 and 3 to form 6. Then you can join 6 and 4 to form 10. Finally, you can join 10 and 5 to form 15, resulting in one server with processing power 15. Consider the array [5, 4, 3, 2, 1]. You cannot join any servers because the processing power is always decreasing. Consider the array [1, 3, 2, 4, 1, 5]. You can join 1 and 3 to get 4. You cannot join 4 and 2. You can join 2 and 4 to get 6. You cannot join 6 and 1. You can join 1 and 5 to get 6. The final servers will be [4, 6, 6]. Write a function that takes an array of integers representing server processing powers and returns the final array of server processing powers after performing all possible joins.

Amazon

Let's explore your problem-solving skills with a practical scenario. Imagine you're building a feature for an e-commerce website that recommends products to users based on their past purchases. The system currently uses a simple algorithm: it identifies the products most frequently bought by users with similar purchase histories and suggests those. However, you've noticed a few issues: Lack of Novelty: The recommendations are often too obvious or repetitive. Users who bought coffee are constantly recommended coffee, even though they might be interested in other related items like filters, mugs, or coffee makers. Cold Start Problem: New users with limited purchase history receive generic recommendations that aren't very helpful. Ignoring User Context: The system doesn't consider factors like the time of year (e.g., recommending winter coats in July) or current trends. Your task is to enhance the recommendation system to address these issues. Describe your approach, considering both algorithmic improvements and data enrichment strategies. Provide specific examples of how you would: Incorporate a measure of diversity to introduce novelty into the recommendations. Handle the cold start problem for new users. Leverage additional data sources (e.g., product descriptions, user reviews, social media trends) to improve recommendation accuracy and relevance. Explain what machine learning algorithms and evaluation metrics you would use to validate your improvements. How would you A/B test your changes to ensure they are positively impacting user engagement and sales?

Amazon

Tell me about your work experience and current project.