Software Development Engineer - Machine Learning, Ad Response Prediction

Amazon

You are given an array of integers. Write a function to find the contiguous subarray with the largest sum and return that sum. For example: Given the array [-2, 1, -3, 4, -1, 2, 1, -5, 4], the contiguous subarray [4, -1, 2, 1] has the largest sum = 6. Given the array [1, 2, 3, 4, 5], the contiguous subarray [1, 2, 3, 4, 5] has the largest sum = 15. Given the array [-1, -2, -3], the contiguous subarray [-1] has the largest sum = -1. Your function should be efficient and handle arrays with both positive and negative numbers. Explain the time complexity of your solution. Can you implement this using dynamic programming? Are there other approaches? Discuss the tradeoffs of the various potential solutions.

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 yourself. To give you some guidance, please share information beyond just your resume. For example, what are you passionate about, what motivates you, and what are some of your long-term career goals? Think about some significant accomplishments you are proud of and how those experiences have shaped you. What makes you a unique candidate and a good fit for our company, beyond your technical skills? I'm looking for a comprehensive overview of who you are, both professionally and personally.