Discuss a scenario where you had to adapt an existing algorithm to better suit the project requirements. What changes did you make and why?

Sample answer to the question

Sure, there was this one time when I had to tweak an algorithm for a school project. It was a simple pathfinding algorithm - A*. The issue was that it was too slow for the real-time analysis we needed. What I did was alter the heuristic function to reduce the number of nodes it considered. Instead of just Euclidean distance, I added a factor that considered the density of obstacles around the path. This significantly boosted the performance because the algorithm could now smartly avoid areas with high obstacle density, which potentially reduced the number of paths it had to explore.

A more solid answer

In a previous project, we were building an e-commerce recommendation system where I took the initiative to enhance the existing collaborative filtering algorithm. Initially, the recommendations were generic and didn't adapt well to user behavior over time. To tackle this, I integrated a decay factor into the algorithm to give more weight to recent user interactions. I rewrote a segment of the code in Python to incorporate this new feature, experimenting with different decay rates, and conducted A/B testing to measure improvements. The results were immediate - there was a 20% increase in click-through rates. I collaborated closely with our data science team to analyze customer data and ensure my changes aligned with project goals, emphasizing adaptability in learning from their feedback.

Why this is a more solid answer:

The solid answer improves upon the basic answer by incorporating more details relevant to the job description, such as specific programming skills (rewriting code in Python) and teamwork (collaborating with the data science team). This answer also displays the candidate's analytical thinking by using A/B testing to measure the algorithm's improvements. However, there is room to enhance the answer further by demonstrating effective communication skills more clearly and detailing the candidate's time management and organizational abilities during the project.

An exceptional answer

On one of our projects at XYZ Tech, we were revamping a facial recognition system which relied heavily on a convolutional neural network (CNN). My task was to optimize the existing algorithm for better accuracy and speed, as it was lagging behind our competitors. Delving into the data, I identified that the CNN was misinterpreting features in low-light conditions. To improve this, I implemented a pre-processing normalization that adjusted image contrast before feeding them into the network. Introducing parallel processing for the data-hungry segments of our code in C++ also reduced the recognition time by 35%. I worked within a tight deadline, managing my tasks with precision to ensure that no part of the project was delayed. Throughout the process, my communication with the testing and product teams was vital to understand the real-world implications of algorithm changes. This role taught me to quickly adapt and learn new image processing techniques to stay ahead in the evolving field of AI.

Why this is an exceptional answer:

The exceptional answer demonstrates a deep understanding of the candidate's role and the project needs. It reflects multiple skills from the job description, such as algorithm design and analysis (optimizing CNN), programming (parallel processing in C++), analytical thinking (identifying issues in low-light conditions), and effective communication (interacting with different teams). This answer also highlights adaptability in learning new techniques and strong time management. It places the candidate as proactive and deeply involved in solving complex problems, showcasing a commitment to innovation and quality.

How to prepare for this question

Analyze the job description to understand key skills required such as algorithm design, programming, collaboration, and communication. Reflect on past experiences that demonstrate these skills.
Develop a structured response that showcases specific examples of algorithm adaptation, including the thought process behind it, the changes made, and the impact of these changes.
Practice communicating technical details in a comprehensible way that can be understood by both technical and non-technical audiences.
Prepare to discuss how you manage your time and work collaboratively in a team setting, as teamwork and time management are critical components of the job description.
Be ready to talk about your adaptability and eagerness to learn. Highlight any learning experiences from past projects that involved picking up new technologies or techniques.

What interviewers are evaluating

Algorithm design and analysis
Programming and software development
Analytical and critical thinking
Effective communication
Time management and organization
Adaptability and eagerness to learn new technologies