The Art of Nailing Data Science Job Interviews - Tech Magazine

The Art of Nailing Data Science Job Interviews

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The secret to crack a data science job interview lies within how well you have prepared your resume and how confidently you face the interviewers. Also, it is imperative to know about the expected question while in-person panel interviews before appearing for the entry-level data science job interviews. It’s not just about your knowledge, but also how you showcase your knowledge that determines your success or failure in a data science interview. Right from the underpinnings to the advanced concepts of data science- you should have adequate knowledge of the entire subject to nail an interview. Let’s go through the information furnished below that might help you a lot to attain a job offer for data scientist role.

Scope in Data Science

Data science has emerged as a very lucrative career option since the companies now splurge a lot of money on constituting the team of data scientists. There has been a crunch in data science competence as the technology is new and therefore, the market demand for data scientists always continues to rise.

There are the online data sciences courses like data science bootcamp that not only help you transform as a data scientist but also offer job guarantee. With a computer science background, an engineer can kick-off his data science learning with the easy availability of such courses.

According to John Foreman, VP- Product Management, MailChimp, it is always challenging to find and hire apt data scientists because of multi-disciplinary nature of this technology. In such a scenario, if you know the most possible interview question, then you inch closer towards nailing data science job interviews.

Candidates that fit the data scientist role  

What a company commonly looks in an entry-level data scientist is a person with 3-4 years of experience as a Business Analyst who holds a good command over Python, SQL, and data visualization. Of course, the person should be willing to move to the data science field. The senior software engineers seeking the opportunities to work on machine learning and analytics projects are also fit for entry-level data scientist role.

Resume guidelines for data science job applicants

Resume is the face of an applicant, which carries the details of their expertise and professional accomplishments. Hence, knowing the guidelines for a resume before diving deep into questions is always wise.

  1. Keep it a one-page resume- Employers doesn’t have the whole day to flip the pages of your resume. Keep in a one-page resume with details of your email ID, GitHub account and LinkedIn account.
  2. Showcase your recent projects– It is always beneficial to describe your recent projects and technologies you deployed to deliver the same. If possible, show the project outcomes as well.
  3. Describe your skills– Highlight your key skills around data manipulation, organization and database along with the key projects you delivered, employing these skills.
  4. Mention your certification/ attended bootcamps– Don’t skip mentioning your earned certifications or any hackathons or bootcamps you had attended in the past. It shows your dedication to learning the technology.
  5. List down your academic credentials- Your educational background should also be part of your resume.
  6. Make your resume searchable– Use the industry-oriented keywords in your resume, like machine learning, analytics, Python, SQL, NoSQL, automation, etc. to make it more searchable.

Let’s take a quick tour to the interview questions

Being an applicant for entry-level data scientist job, you need to prepare yourself for the in-person data science interview to be conducted by a panel. We have listed down the most probable questions for data science job interview below to help you nail the same easily.

Possible Questions by Data Science Vice President

  • How do you define data science?

Ans- Data Science is a science which involves automated methods to extract, interpret, analyze and gather humongous data from multiple sources. It combines the different aspects of computer science, statistics, visualization, etc. This is the only technique to derive new insights and findings in a digital format from bulk data.

  • How was your previous work experience? Did you face any issue in previous job?

Ans- This answer is based on your experience but make sure to portray your previous work experience positively. When it comes to issues, talk about the challenging nature of your previous job roles.

  • Let’s discuss your last project.

Ans- This question aims to know your skills and expertise, so respond wisely and honestly.

  • How do you differentiate between supervised and unsupervised learning?

Ans- Input data is labeled in supervised learning whereas in unsupervised learning, input data is not labeled.

Supervised learning is based on training dataset. On the other hand, unsupervised learning uses input dataset.

Supervised learning is meant for prediction whereas unsupervised learning is employed for analysis.

Supervised learning enables regression and classification. Unsupervised learning enables classification, density estimation, & dimension reduction.

  • Define logistic regression.

Also called logit model, logistic regression is a process to analyze the binary outcome from the combination of predictor variables.

  • Can you give any example of logistic regression?

Answer- Suppose, we need to predict if India will win a cricket match against Australia or not. In this case, the prediction outcome would be binary which is 0 or 1 (Win or Defeat). The predictor variables here would be India’s past record against Australia in Test Cricket, India’s performance on that particular ground, players’ current records, etc.

When it’s between Sr. Data Scientist V/S Cub Data Scientist

The questions from data scientist might be casual, revolving around your past projects & their outcomes, challenges you undertook while working in the previous organization, etc. They might also discuss their current data science team size & structure, projects they’re handling and their project priorities. The possible questions from data scientist may include:

  • Why do we perform A/B testing?

Ans- A/B testing is mainly performed to derive the outcomes of the changes done in a web page. While conducting A/B testing, the two versions of a web page are named as A and B. Both the variants are made available among same visitors at the same time. The page with better conversion is declared the winner.

  • Which language you prefer for text analytics- Python or R and why?

Ans- Python would be more preferable for text analytics.

Python has Panda library for easy to use data structure and use of high-end data analytics tools whereas R is better for machine learning than only text analysis.

And, the second thing is that Python is the fastest for text analytics.

  • Is data cleaning helpful in the analysis?

Ans- Yes, undoubtedly it helps in multiple ways.

A refined and cleaned data can be arranged in a format which is loved by data scientists as well as analysts to work with.

Data Cleaning is helpful in machine learning by increasing the preciseness of model.

Though it’s a time taking process to clean the data, but it makes the analysis task more precise and easier.

  • Can you define cluster sampling?

Ans- It’s a sampling method wherein the whole population is separated into groups by the researchers and these groups are called cluster. Then, the researcher randomly picks any of the clusters from the population and conducts the analysis from that cluster.

  • I hope you know about systematic sampling as well.

Ans- Yes, sure! Unlike cluster sampling, this process of sampling the data involves creating the list of each member of the population individually. Afterwards, the first sample elements from the first kth element are randomly picked from the population list. And thereafter, selection of every kth element on the list is done repeatedly.

  • How do you differentiate the Validation Set and Test Set?

Ans- Validation Set is a sort of training set as it is used to avoid the model over-fitting as well as a selection parameter.

Whereas the test set is employed by data scientists to assess or evaluate the ability of a well-trained model of machine learning.

  • What is cross-validation?

Ans- Cross validation is one of the model validation techniques, which is basically employed to generalize the statistical analysis to transform it into an independent set of data. It is mainly used where prediction is the aim and one wants to assume the accuracy of a predictive model in practice.

Cross-validation is performed to check the performance of model in the training stage only to prohibit the further issues.

Few more possible generic questions by Senior Data Scientists

  1. Have you ever introduced any out-of-the-box idea which helped in project progress? If yes, then kindly explain.
  2. Do you know how to develop A/B test?
  3. Can you tell us about the largest number of data handled by you and your data-handling results?

What usually Chief Technical Officers (CTO) ask?

  1. Any entrepreneurial experience you want to share with us…
  2. List down the areas where you need more development.
  3. Why did you leave your last job/ why do you want to leave your current job?
  4. Why do you want to join us?
  5. Throw some light on your key learning points in your previous company.
  6. How has been your data science journey till now?

What VP Marketing has to interview you?

It’s quite obvious to assume that the questions from Marketing VP will be more business-centric. These questions may include but not limited to:

  1. Why do you want to join our organization?
  2. How was your previous job role? Did you have interactions with Sales/ Marketing people in last company?
  3. Do you know how to communicate your insights through visual presentation?
  4. Have you ever been engaged in data science activities apart from your regular job?

Thins you need to confirm

(To the head of data science unit) If I get the job, what projects you would assign me if you don’t mind telling.

(To the HR) I hope my resume displays my skillset and experience well. Don’t hesitate in asking any questions about my qualifications and experience, if you feel so. I can address all your queries.

The Final Tip

Data Science is a soaring field and you don’t need to sell yourself short. Discuss the paychecks and perks confidently with the HR department.

Data Science has a sea of opportunities for the technology-freaks. Pull up your socks to become a data scientist if even after being a computer science professional, you are not conversant with this emerging technology. Springboard is one such place that has introduced Data Science Career Track with 100% job guarantee, which is an online data science course to future-proof your career.

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