Data Engineer - Global Accounts, Professional Services, AWSI-SDT-APJ-Japan

Amazon

You are given a string moves of length n consisting only of characters 'L', 'R', and '_'. The string represents your movement on a number line starting from the origin 0. In the ith move, you can choose one of the following directions: move to the left if moves[i] = 'L' or moves[i] = '_' move to the right if moves[i] = 'R' or moves[i] = '_' Return the *distance from the origin* of the *furthest* point you can get to after n moves. Example 1: Input: moves = L_RL__R Output: 3 Explanation: The furthest point we can reach from the origin 0 is point -3 through the following sequence of moves LLRLLLR. Example 2: Input: moves = R__LL Output: 5 Explanation: The furthest point we can reach from the origin 0 is point -5 through the following sequence of moves LRLLLLL. Example 3: Input: moves = _ Output: 7 Explanation: The furthest point we can reach from the origin 0 is point 7 through the following sequence of moves RRRRRRR.

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

Describe a time when a plan did not go as expected.