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Data Scientist Interview Questions

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  1. What tools or devices help you succeed in your role as a data scientist? See answer
  2. How do you identify a barrier to performance? See answer
  3. Why do you want to work at this company as a data scientist? See answer
  4. How has your prior experience prepared you for a role in data science? See answer
  5. What methods do you use to identify outliers within a data set? See answer
  6. Data scientists need to be able to develop their own questions that guide their research. What is your process for developing questions? Can you provide an example from your previous role? See answer
  7. How do you overcome challenges to your findings as a data scientist?
  8. How do you clean up and organize big data sets?
  9. Are you familiar with exploding gradients? What does this term represent and how does it compare to standard gradients?
  10. Can you tell me about the decision tree algorithm and when it’s used?
  11. What are a few examples of branches in machine learning?
  12. What is your level of experience with computer programming? Which programming languages do you have the most experience with?
  13. If you had to choose one algorithm to analyze data, what would it be and why?
  14. How do you differentiate between machine learning and deep learning? Can you provide examples?
  15. You create a data storage system to organize data figures, but it isn’t working correctly. Are you comfortable asking others for help?
  16. What is the confusion matrix used for? Can you provide an example?
  17. How do you decide which models or algorithms to use in analyzing data sets?
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8 Data Scientist Interview Questions and Answers

Q:

What tools or devices help you succeed in your role as a data scientist?

A:

This question’s purpose is to learn the programming languages and applications the candidate knows and has experience using. The answer will show the candidate’s need for additional training of basic programming languages and platforms or any transferable skills. This is vital to understand as it can cost more time and money to train if the candidate is not knowledgeable in all of the languages and applications required for the position. Answers to look for include:

  • Experience in SAS and R programming
  • Understanding of Python, PHP or Java programming languages
  • Experience using data visualization tools
Example:

“I believe I can excel in this position with my R, Python, and SQL programming skill set. I enjoy working on the FUSE and Tableau platforms to mine data and draw inferences.”

Q:

How do you identify a barrier to performance?

A:

This question will determine how the candidate approaches solving real-world issues they will face in their role as a data scientist. It will also determine how they approach problem-solving from an analytical standpoint. This information is vital to understand because data scientists must have strong analytical and problem-solving skills. Look for answers that reveal:

  • Examples of problem-solving methods
  • Steps to take to identify the barriers to performance
  • Benchmarks for assessing performance
Example:

“My approach to determining performance bottlenecks is to conduct a performance test. I then evaluate the performance based on criteria set by the lead data scientist or company and discuss my findings with my team lead and group.”

Q:

Why do you want to work at this company as a data scientist?

A:

The purpose of this question is to determine the motivation behind the candidate’s choice of applying and interviewing for the position. Their answer should reveal their inspiration for working for the company and their drive for being a data scientist. It should show the candidate is pursuing the position because they are passionate about data and believe in the company, two elements that can determine the candidate’s performance. Answers to look for include:

  • Interest in data mining
  • Respect for the company’s innovative practices
  • Desire to apply analytical skills to solve real-world issues with data
Example:

“I have a passion for working for data-driven, innovative companies. Your firm uses advanced technology to address everyday problems for consumers and businesses alike, which I admire. I also enjoy solving issues using an analytical approach and am passionate about incorporating technology into my work. I believe that my skills and passion match the company’s drive and capabilities.”

Q:

How has your prior experience prepared you for a role in data science?

A:

This question helps determine the candidate’s experience from a holistic perspective and reveals experience in demonstrating interpersonal, communication and technical skills. It is important to understand this because data scientists must be able to communicate their findings, work in a team environment and have the skills to perform the task. Here are some possible answers to look for:

  • Project management skills
  • Examples of working in a team environment
  • Ability to identify errors
Example:

“My experience in my previous positions has prepared me for this job by giving me the skills I need to work in a group setting, manage projects and quickly identify errors.”

Q:

How do you overcome challenges to your findings?

A:

The reason for asking this question is to discover how well the candidate approaches solving conflicts in a team environment. Their answer shows the candidate’s problem-solving and interpersonal skills in stressful situations. Understanding these skills is significant because group dynamics and business conditions change. Consider answers that:

  • Encourage discussion
  • Demonstrate leadership
  • Acknowledges recognizing and respecting different opinions
Example:

“I would acknowledge the validity of their findings. Then I would describe how I came to my conclusions using my data. I would also invite an open discussion of the results.”

Q:

How do clean up and organize big data sets?

A:

Data scientists frequently have to combine large amounts of information from various devices in several formats, such as data from a smartwatch or cellphone. Answers to this question will demonstrate how your candidate’s methods for organizing large data. This information is important to know because data scientists need clean data to analyze information accurately to offer recommendations that solve business problems. Possible answers may include:

  • Automation tools
  • Value correction methods
  • Comprehension of data sets
Example:

“My data cleanup techniques involve determining if the data that I am collecting makes sense and correcting any values that are not logical after I have adequate information. I also use tools to help automate the cleanup process, such as Paxata.”

Q:

What methods do you use to identify outliers within a data set?

A:

Data scientists must be able to go beyond classroom theoretical applications to real-world applications. Your candidate’s answer to this question will show how they allocate their time to finding the best way to detect outliers. This information is important to know because it demonstrates the candidate’s analytical skills. Look for answers that include:

  • Raw data analysis
  • Models
  • Approaches
Example:

“I like to use practical methods and analyze the raw data first. I will then think about which model will help me to detect any outliers.”

Q:

Data scientists need to be able to develop their own questions that guide their research. What is your process for developing questions? Can you provide an example from your previous role?

A:

Unlike Data analysts who research and sort through data based on company management's questions, data scientists are responsible for generating their own questions to contribute to various company knowledge. This question allows interviewers to gauge a candidate's creativity and their ability to develop questions that relate to company operations or industry trends. A candidate's answer should emphasize:

  • Creative-thinking skills
  • Knowledge of their employer and their industry
  • Communication with team members
Example:

"First, I review changes to company operations or procedures, research industry trends and look over company products or service offerings. I also consult with department heads or management professionals within the company to see if they have current issues or concerns. I use my findings to generate a few key questions to guide my research. If I have difficulty developing questions, I seek advice from other data professionals. In my previous role, I wanted to determine whether my employer's marketing campaigns received more engagement than their competitors. I chose two campaigns that launched within the same time frame and reviewed sales data over the span of four months."

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