Secrets to a Successful Data Science Interview

Please go ahead, dazzle the interviewers with your DL skills, but after you’ve proven a point or two with your ML chops.

You should know why you chose one model / algorithm / approach / architecture above others.

Hyperparameters play a key role in DL.

Develop a sound understanding of model tuning.

Difference between a good model and a not-so-good one may lie in your choice of hyper parameters.

We recommend below material to go through before going for interviews:Note: We didn’t say you *must* go through all of these.

Acquiring familiarity with the subject by perusing high quality content gives you confidence that is transferable across situations.

   Companies give priority to candidates who not only articulate good data science solutions but also can efficiently implement using the right tools.

It will be great if you can make yourself comfortable with the tools available in the industry.

You should be at least aware of python or R from the language point of view, scikit learn library for ML algorithms, Keras / tensorflow / pytorch / caffe for deep learning.

Do not neglect querying languages eg.

HQL,SQL and distributed frameworks like hadoop and spark.

Good organizations have training programs for all these skills.

Selected candidates often go through these training courses.

Familiarity with the above tools gives your candidature the much needed edge.

By all means undergo training after you get selected.

This will give exposure to the problems the organisation solves and opportunity to interact with other scientists in the organization.

These are very important experiences to acquire as a new-joiner.

   Interviewers expect you to know how a machine learning model is used, how it is productionised and deployed and the overall end-to-end architecture of your model.

It is very difficult to hire a person who does not know how his model will be used by the client/service/customer/product.

Without deployment, what you have built is a POC.

At the very best, it works on your laptop for demo purposes.

For your ML model to be useful it needs to be part of a software pipeline.

Know how to interact with engineers towards deploying models.

Be prepared to roll up your sleeves and get into the act without anyone prompting you.

By doing so, you are enhancing your value and standing in the team.

   As a great data scientist, you should understand the full life cycle of the ML model.

You should know how your model should change when the world it tries to model changes.

(This happens more frequently than you may think, for the real world isn’t under your control.

) You will decide how frequently your models need to be retrained for them to remain fresh.

You may also want automate the retraining process for saving your precious time.

Re-purpose the time saved in iterating model building towards improving performance.

Create opportunities for yourself to explain how you’ve managed a model through its life cycle.

   Debugging is a very important skill in software industry.

A software is highly risky if it cannot be debugged.

You should understand your model in depth.

You are expected to be aware of the internals of the algorithms that you have used.

You should know how to do root cause analysis and debug your models.

Interviewers expect you to know how you will improve the results when models have high bias or high variance, what you will do to avoid exploding gradients and vanishing gradients and how you will optimize memory during training etc.

   While it is true that Data Structures and Algorithms are outsourced to packages, your ability to make inferences from data is aided by your understanding of algorithms and data structures.

While interviewers won’t ask you to implement skip lists or balance k-d trees, they would still expect you to understand order complexity of algorithms, be familiar with basic data structures such as linked lists, stacks, trees, hash-tables and heap, and be comfortable with algorithms such as sorting, shortest paths, string processing and the like.

In other words, hygiene questions from data structures and algorithms.

You may think it is inappropriate to judge you through this lens, but remember you are making it easy for the competition.

We are sure you are more than up to it.

   The very fact that you have been called for an interview is an indication that your prior experience has been considered as a potential fit by the HR and by the HM (hiring manager).

Don’t stop there!.It is your responsibility to research and know what the group you have been called for interviewing does and how you can add value — this is a great distinguisher.

   Beware, this is no invitation to cozy up with the interviewer.

Be formal and polite as you’ve been throughout the interview.

This is a great opportunity to display your understanding of how to function as part of a data science team.

It’s also an opportunity to direct the interviewer to your strengths that weren’t covered in the interview.

For example you might be good at code reviews.

You could enquire how code review works.

You may highlight your favourite approach, say walkthroughs.

You might want to know the dev platforms used.

You may ask if there are any open source contributors in the team (this is the time to re-emphasize if you are one).

You could ask about the delivery cycle.

If the team is distributed across timezone, check how the interactions work.

Share your experience working in such teams if applicable.

Ask if it’s OK for you to know at a high level the project(s) the interviewer is part of.

These are far better questions to ask than wanting to know about the WFH policy or how the typical day for the data scientist is like.

Phrase these questions carefully, for you aren’t the interviewer!.Of course, it’s best to ask just two questions as the conversation that ensues do not give you more openings than that.

So chose them carefully and be guided by the interview context.

Most importantly, be a patient listener, that’s a key to memorable conversations.

Note: Don’t try to hypnotise the interviewer by going over the top.

As always, be dignified.

Remember, no job is greater than your dignity.

  It is honourable to admit that you are not very familiar with the topic.

You may still want to try based on first principles if possible and also ask clarifying questions.

Else no worries, the interviewer will move to another topic from your resume.

   In case you do not make it this time, rest assured the interviewers didn’t do it for fun.

Companies invest serious time and money in evaluating you, and if you do not make it, it simply means that you need more preparation.

 Youare not rejected, only your application is.

Also, consider the fact that, in addition to you, many other smart and capable candidates are interviewed for the position you have applied for.

It is in the very nature of the process to select only a few candidates.

If it is not you, it is not the end of the world for you.

(Frankly, do not give anyone that kind of power over you.

) Note down the questions that were asked of you without any judgment.

But don’t analyse yet; allow one or two days to pass for you to regain your composure.

Then reflect without rancour how you could be a better candidate next time.

Identify opportunities for improvement and put in a plan of action, and start acting on it, for it is of no use living in the past or in a state of inaction.

Companies very much expect you to apply again in the next cycle (check with the HR about the period) –many of us have applied more than once before getting selected.

Your HR contact also would strive to provide feedback, but please… please… please… do not start a rebuttal chain.

Behave like a data scientist in such situations: If the data (interview result) is at variance with your hypothesis (your preparation), move to a better hypothesis, i.

e, be better prepared next time.

OK, this may sound a little philosophical.

However we think it is practical and important in making you a better candidate and a better person in general.

Develop two kinds of self-awareness: Internal and External.

Former is about how aware you are of yourself; latter is about how aware you are of others’ perception of you.

It should be clear as to how these two come together in creating a fruitful interview experience for you.

We recommend you to go through Insight by Tasha Eurich.

   You are perhaps thinking, this seems like a wordy and a long list filled with synthetic friendliness sprinkled all over that places a lot of demands on me in a contrived manner (that isn’t the intention, and it is not artificial friendliness either; sorry about the wordy part though).

You might be wondering what exactly you would get in return as part of a fair interview process.

Here’s what we offer to our candidates.

You may expect similar experience from other reputed organizations as well.

      Wish you a great preparation.

May the Force be with you!!!We would love to hear your feedback.

Don’t forget to share your experiences with us on how these tips helped you in cracking the data science interview.

  Disclaimer: Above views are personal and not to be construed as our organization’s stated position.

However, we do expect any professional organization to have a fair selection process that is reflective of the points mentioned above.

  Himanshu Jain is a Data Scientist / Machine Learning Engineer at Walmart Labs.

Suresh Venkatasubramanian is Principal Data Scientist at Walmart Labs.


Reposted with permission.

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