Data science interview questions will help you understand if a candidate possess certain traits that are required to complete the job or not. Usually the questions are created and asked according to job role. While the HR's ask simple questions related to past relevant experience, if a candidate is comfortable to switch to a particular location, salary and notice period.

But technical recruiters and hiring managers need to ask the right questions to ascertain the skills and expertise knowledge of a candidate before data scientist hiring.

**Why it’s so important?**

1. Companies are usually not able to reap the benefits of a data scientist for their own advantage. This leads to wrong hire with right skills.

2. Also, hiring a data scientist with the right skill set according to job role is difficult.

3. The demand of data scientists exceeds supply, so hiring good candidates faster is important.

4. Fake data scientists and wrong hiring costs companies more than $110,000 every year.

5. Right task must be given to data scientists to make the full use of their expertise.

The bothersome responsibility is how to hire and assess them because the competition for data science talent is fierce.

Do remember, all the good candidates are either taken or will be if you lose valuable time.

In a competitive field like data science, strong candidates often receive a lot of offers, some even get around 5-6 offers every month. So, to hire the right candidates faster you need to assess them quickly.

Experts have different opinions when it comes to data scientists, some believe many don’t understand the meaning and give wrong tasks, some believe it’s a hyped up term , while others believe data scientists are hired to do the job of 2 employees.

There are also many like me who believe data scientists possess most versatile skills by being the best in some, good in others and average in a few but never bad in any otherwise they should surely change their profession.

So, data scientist interview preparation should start with first checking the job description and skills required to cater that job as that will be the base of questions you want to ask.

Below data science interview questions will surely help you assess data scientist.

Before I continue with questions I must say, you can find these questions easily on Google and that means your candidate can find these questions too just like you did.

So if you are really serious about hiring the data scientist who is a skilled job fit candidate, I would strongly recommend you to check our data science test which is for HR’s only.

## Data Scientist Interview Questions Are Categorized Into 3 Main Parts

**I) Statistical data scientist interview questions based on math, machine learning, probability etc.**

**II) Technical data science interview questions based on data science programming languages like Python, R, tableau etc.**

**III) Face to Face Interview usually have data science interview questions related to past experience or projects.**

### I) Statistical Data Science Questions

Start with some basic questions to help your candidate understand what job role they are getting into. HR’s or Hiring Managers usually skip this part and later candidates are not satisfied with the job and usually leave.

The main reason is data scientist usually feel like they are going to make a lot of contribution to any company they join and that may or may not be achieved according to a company’s requirement.

**1. “Your role will require to analyse, visualize and maintain data in the first few months, will you be fine with that?”**

Check Theory to Detect Fake Data Scientist. Hiring Managers will surely know these answers but HR’s can look for certain terms that will help you assess.

**2. “Do you understand the term regularization, tell me how useful it is?"**

Look for terms like - tuning parameter | Over lifting | Constant Multiple | Existing Weight Vector.

**3. “How will your role be different from ML or AI**” (Machine Learning – Artificial Intelligence)

Answer will vary so just keep in mind the definition of Data Science, ML & AI.

**4. “Which technique will you use to predict categorical responses?”**

Answer should contain - Classification methods which are widely used to predict binary or multi class target variable.

Look for terms like - conventional parametric models | multinomial regression |

Linear discriminate analysis | non parametric techniques.

**5. “Why normal distribution is important?”**

Look for terms like - Central limit theorem | independent variables | statistics.

**6. “Can you talk on Eigenvalue and Eigenvector?"**

Look for terms like - linear transformations | correlation or covariance matrix | compressing | compression.

**7. “Explain about the box cox transformation in regression models.”**

Look for terms like - response variable | skewed distribution | statistical technique | non-normal dependent variables.

**8. Can you use machine learning for time series analysis?**

Answer would be yes although the approaches can be different according to applications.

**9. “Do you know the ways to perform logistic regression with Microsoft Excel?”**

There are two answers to this-

- Use fundamentals of logistic regression and use Excel’s computational power to build a logistic regression
- Use Add-ins provided by third parties.

**10. “ Do you know the formula to calculate R-square?**

Formula - 1 - (Residual Sum of Squares/ Total Sum of Squares) or R-squared = 1 – (First Sum of Errors / Second Sum of Errors).

**11. “Explain what precision and recall are. How do they relate to the ROC curve?”**

Answer will have four results:

- TN / True Negative: case was negative and predicted negative
- TP / True Positive: case was positive and predicted positive
- FN / False Negative: case was positive but predicted negative
- FP / False Positive: case was negative but predicted positive

### II) Technical data scientist interview questions

To check programming and coding knowledge that may be of python or tableau or any other language the best way is to check practical knowledge through assessment. Otherwise allow the hiring manager to ask the below questions although its not successful process as its a time consuming process and requires answering it in a specific way which is not easy plus the chances of biased interviews increases.

The easy way would be to use an assessment tool to hire data scientists.

Questions Related To NumPy, Djanjo, Python Data Science Questions -

**1. What is monkey patching and is it ever a good idea?**

**2. How do you keep track of different versions of your code?**

**3. What is the difference between list and tuples?**

**4. How is memory managed in Python?**

**5. Explain what Flask is and its benefits?**

**6. Explain how you can set up the database in Djanjo**

**7. What advantages do NumPy arrays offer over (nested) Python lists?**

**8. How do you make 3D plots / visualizations using NumPy / SciPy? **

### Questions Related to R Data Science Interview Questions -

**1. What are the similarities and differences between R and Python?**

**2. What are the different data types in R?**

**3. Why use R?**

**4. Why would you use factor variable?**

**5. How do you concatenate strings in R?**

**6. How many sorting algorithms are available in R?**

**7. Do you know how to make an R decision tree?**

**8. Can you use R to predict data analysis?**

**9. How missing values are represented? **

**10. Explain what is transpose.**

**11. What are the top 2 advantages of R?**

**12. The memory limit of R is 3Gb or 8 Gb? ()**

**13. Can you tell me the 6th sorting algorithm? (none)**

**14. Can you create new variable in R programming?**

**15. What is the use of coin package in R?**

**16. What is the of use Matrix, doBY, Forecast, MASS and MATLAB package?**

**17. What is logistic regression in R? **

**18. What is iPlots?**

If you are looking to ask questions related to Tableau -

**1. What is the difference between context filter to other filters?**

**2. Max no of tables we can join in Tableau?**

**3. What are Dimensions and Facts?**

**4. What is dual axis & blended axis?**

### III) Face to Face Data Science Questions

I strongly believe face to face interviews are more about understanding your candidates, so questions should move from theoretical questions to discussion. This will help you to assess candidates expertise and business understanding, Yet, a few more questions won't harm.

**1. In which libraries for Data Science in Python and R, does your strength lie?**

**2. Suppose you are given a data set, what will you do with it to find out if it suits the business needs of your project or not.**

**3. What unique skills you think can you add on to our data science team?**

**4. Is more data always better?**

**5. What are your favorite data visualization tools?**

**6. What do you think is the life cycle of a data science project in our company?**

**7. Which is better - too many false positives, or too many false negative? You can give examples.**

**8. How do clean up and organize big data sets?**

**9. What opportunities data science will bring in the near future?**

**10. Ask industry-specific questions related to data types, domain knowledge etc.**

**11. What were the business outcomes or decisions for the projects you worked on?**

**12. What’s your favorite part of being a data scientist?**

The last 2 questions are the most relevant out of all the other questions listed above.

It’s important to understand what your candidate enjoys the most being a data scientist and what they were able to achieve in their last job. This helps to understand if they are up for the new challenges or perform well.

These are the best data scientist interview questions you can ask or will find in internet.

**Period:**

All in all the above data scientist questions will help you know if a candidate has skills and expertise for a particular job role.

But do remember the end game is to recruit the right candidate and the best way to assess is by giving candidates data scientist’s assessments in the initial stage.

Make a quick call to know their availability, ask them to appear for a quick assessment that assesses all their skills, get results, compare the candidates and interview the ones who score high. That will help you recruit right.

Do comment your suggestions for any other important data science questions you feel are important and must be included.