5 (Un)Conventional Interview Tips For Data Scientists And ML Engineers
By: Richmond Alake
Tags: Machine Learning, Data Science, Office Hours, Artificial Intelligence, Advice

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Machine Learning topics are highly technical and straight forward when discussed. What I mean is that when questions are asked, there tends to be little to no ambiguity to answers that could be given.

You either know what you are talking about or you don’t.

In some interview scenarios, there’s little to no room to have an extended discussion around ML topics, especially when an interview feels like an interrogation with the several “What is” and “How Do I” type questions.

There’s nothing wrong with having direct answers when asked questions such as “What is a neural network” or “How do you prevent a network from overfitting”.

Although, direct answers can showcase your knowledge and in some cases, expertise; they fail to showcase your personality, experience and creativity.

Well prepared interviewers have other questions or methods of gauging your personality and other social factors.

But for those who don’t, you might come across as typical or nothing out of the ordinary.

One approach that has worked for me is having a conversation around a topic that’s asked within questions.

To give an example of how you can initiate conversations around interview questions, take a look at the two scenarios described.

Scenario 1

Interviewer: Could you explain what overfitting is and how you would prevent it?

Candidate 1: Overfitting occurs when the machine learning model does not generalise accurately to unseen data. This usually happens when the model learns predominantly patterns, including noise, that are present within the training data. Overfitting is fixed by reducing the number of features in the training data or reducing the complexity of the network through various regularisation techniques.

Scenario 2

Interviewer: Could you explain what overfitting is and how you would prevent it?

Candidate 2: Overfitting occurs when the machine learning model does not generalise accurately to unseen data. This usually happens when the model learns predominantly patterns, including noise, that are present within the training data. Overfitting is fixed by reducing the number of features in the training data or reducing the complexity of the network through various regularisation techniques.

Could I ask, What methods to prevent overfitting do data scientists within your team leverage?

Scenario Analysis

The two scenarios described above are very similar, and if you read to quickly, you might have missed the difference between both answers from the candidates.

Candidate 1 answered the question well, and the answer given was relatively detailed.

But, the majority of candidates(not all) taking an interview are very prepared to answer typical ML/DS science such as the one asked above.

My point is, there is nothing that differentiates candidate 1 from the other candidates that could have answered the question as well.

Candidate 2, on the other hand, has posed a question back to the interviewer on the same subject matter, creating an opportunity for a conversation around the topic.

This allows the interviewer to go off-script and ask the more exciting and non-scripted question.

More importantly, ending answers to a question asked by the interviewer with a related question portrays to the interviewer that you are curious and interested in the operations of the team/company already.

I use this technique to break out of an interrogation style interview and move more to a natural feeling conversational interview, where both parties are comfortable.

Remember that interviewers hire people they like.

Having a conversational style interview enables your personality to come across in a more likeable manner.