6 Steps To Take 1 Day Before To Prepare and Succeed in an AI Interview

Author(s): Himanshu Joshi

Originally published on Towards AI.

Strategies to prepare better U+1F64C

Recently I was taking an Interview for a Data Scientist(Machine Learning) position in my company.

The candidate interviewing for the role was not fit. I explained why he was not suitable for the role and told him to apply for a Data Analyst position instead.

But he insisted I tell him how to prepare for his next interview as he wanted to interview for Machine Learning Roles only.

I gave him a few tips on how I usually prepare for interviews.

This is when I thought about recollecting my previous interview experiences (both as an interviewer and candidate) and sharing them with you all (hoping this will help).

So here are a few steps that I took before the interviews that worked for me

1)Get the Job description: Now someone can say why are talking about such obvious things. Everyone does that.

Well, let me tell you that's not the case. Many people just see the designation and come for the interview.

Ask the recruiting person who contacted you for a job description. The thing is in the field of AI especially, for each job title roles and responsibilities differ from company to company.

Ex: A Data Scientist in one company may be building ML models, but in another company, he/she might just be doing data wrangling, while at a different company, the role might just be expected to do visualizations.

Based on the JD, I have decided to go ahead or reject many interview calls.

Saying No is very important.

We don't want to waste our time by giving interviews we won't be interested in.

AI is not as evolved a field as something like Software Engineering etc… many companies themselves are not sure about what kind of candidates they want for various roles. So this helps in filtering out.

2) Check what is expected from the rounds: Another obvious one, right? Unfortunately, not many people do this.

The HRs want you to succeed, so go ahead and ask them all the questions you have. I have seen that most of them are willing to educate the candidates.

Normally there are coding, technical and managerial rounds at many companies in the same order.

Get this information and prepare according to where you are in your interview journey.

I have many times asked HR about the difficulty levels, languages (ex: SQL or Python), etc… This helps immensely in preparing.

3) Ask for information about the Interviewer: In today's day and age, there is all the information available at our fingertips. Let's make good use of it.

An interviewer's name is a great way to check his/her background on sites like Linkedin and get a sense of what they might ask you. Always remember the interviewer will try to gauge you based on what they know. So try to think from their perspective and preempt questions, and be prepared.

I have seen many senior folks post interview questions, and experiences on LinkedIn and other social media sites. This provides great insight into their mindset.

This really has been a game-changer. It provides that extra edge when you know the person's background.

If nothing else at least you get to know if the person is technical or non-technical. This helps in explaining concepts.

4) Create a good story: Every interviewer asks, tell me about yourself.

Keep your story ready. Keep it crisp.

Educational background. Work Experience at various companies and highlight the work in brief related to the role.

Don't fumble at this question. Always keep the story ready.

5) Describe one project in depth: This is another question that is sure to be asked.

I have seen many candidates beating around the bush for this one.

Please understand the interviewer has limited time to take your interview. He/She would want to judge you on various technical parameters.

Don't waste his/her time by beating around the bush. Rather utilize this question as an opportunity to showcase your technical capabilities.

Ex:- For a Data Scientist(Machine Learning) position, you want to prove that you know the whole project lifecycle.

Talk about

a) what was the use case,

b) how you got the data,

c) what EDA you performed (outlier treatment, missing value imputation, etc…)

d) what feature engineering you performed

e) feature encoding (Onehot encoding etc…)

f) Train test split

g) Model building

h) Evaluation Metrics

e) How you found out overfitting and resolve using parameter tuning etc…

f) Model deployment & retraining, if any

By going in such depth, you answer most of the questions about your technical capabilities. Just ensure before you go in such depth to ask which project the interviewer would want a deep dive on.

We don't want them to stop us midway and ask about another project. It becomes a bit embarrassing; this has happened to me once.

Don't let it become a monolog; rather try to engage the interviewer and make it a dialogue by asking questions like does this make sense? Do you want me to explain anything else, etc…

6) Mock interviews: Once you are done with all the above steps, you want to practice them.

Ask your friends to take your interviews, ask them to ask all kinds of weird questions they can think of. This helps us be prepared for questions that can take us off guard.

Believe me, you will get all kinds of weird stuff sometimes to check knowledge and other times as the interviewer also doesn't know what to ask U+1F923U+1F61C

Practice is very important. I have seen that after 3-4 interviews, I don't have to think, words start flowing very naturally. This is probably why they say the more you practice, the luckier you become.

Hope these steps will be helpful and will increase your success rate.

If you are interested in topics like AI, ML & Data Science, do consider following me and check out other stories I have published.

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Published via Towards AI