Having undergone multiple rigorous job interview processes for machine learning engineer and data scientist positions, I have decided to compile a list of the most commonly asked and important questions in MLE interviews. However, it’s important to note that this list is not exhaustive and should only be used as a guide to give you an idea of the types of questions that may be asked. It’s crucial that you conduct further research to fully prepare yourself for your upcoming interviews.
Typically, the interview process for a Machine Learning Engineer (MLE) or Data Scientist role consists of four stages. These include discussions about previous experiences, motivation for the position, behavioral queries, technical questions on statistics and machine learning, and coding assessments. In this post, I’ll focus on the most common and significant questions that candidates can expect in MLE interviews. To make it easier to understand, I have categorized the questions into three sections, namely Statistics & Math, Machine Learning, and Coding & Code Review.
Statistics&Math
- Can you explain what hypothesis testing is? The first thing you should mention is the null hypothesis, which is the statement that you want to test. You should also be familiar with the p-value and how it is used in hypothesis testing. Giving an example to support your answers is always a good idea.
- What is normal distribution, and why is it important, especially for statistical tests? You should also be aware of other relevant distributions, depending on the job description. Additionally, you should understand the relation between normal distribution and the central limit theorem.
Machine Learning
- Can you explain the pipeline of MLE? You should show your knowledge about the overall pipeline of MLE, including CI/CD pipeline and production. This is one of the biggest differences between MLE interviews and Data Scientist interviews. For MLE interviews, there is an expectation to have experience with CI/CD and production.
- What are classification and regression, and what is the difference between them?
- How do you approach an ML problem, and which ML algorithm would you use? You should be prepared for questions about model evaluation, such as how you would evaluate the performance of an ML model/algorithm and which evaluation metrics you would use.
- Can you explain what overfitting and underfitting mean in the context of Machine Learning, and how can they be avoided? You should also understand the bias-variance tradeoff and when an ML model can overfit or underfit in terms of bias and variance.
- What is regularization, and when and how should it be used?
- How do you write tests for ML models?
Coding & Code Review
- Can you solve a coding problem in Python? You should be well-versed in Python, including apply, lambda, groupby arguments, and ML-related libraries such as pandas, numpy, and matplotlib. Practice is key, and you can review notebooks for solutions to ML problems from Kaggle or solve questions from platforms such as Project Euler or LeetCode.
- Can you perform a code review? You should be able to explain what a given function does and offer advice/comments for improving the code’s time and space complexity. You should also be familiar with coding best practices and which built-in functions to use and when.
In conclusion, as the demand for machine learning engineers and data scientists continues to grow, it is essential to equip yourself with the necessary skills and knowledge to succeed in this field. By taking the time to study and prepare for the interview process, you can increase your chances of landing your dream job and making a real impact in the world of technology. So don’t hesitate, start learning and practicing today, and let your passion for machine learning drive you towards a fulfilling and rewarding career.
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