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Data Scientist Interview
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20 Questions and Answers by William Swansen

Updated January 4th, 2020 | William Swansen is an author, job search strategist and career advisor who assists individuals from all over the world.
Question 1 of 20
Data visualization is an important skill that will be used often when communicating results with stakeholders. Describe to me one of your most innovative data visualization ideas that went beyond pie and bar charts.
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How to Answer
Data Science and analysis of complex data sets is a very technical discipline. However, the organization's stakeholders who use the results of the analysis need to be able to clearly understand what the data is telling them and be able to use it to improve their operations and help them make business decisions. You need to be able to present your work in a manner that is easy to understand and utilize. This is known as data visualization. The interviewer is seeking to understand how you organize and present the data to accomplish this objective.
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20 Data Scientist Interview Questions
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Interview Questions
  1. Data visualization is an important skill that will be used often when communicating results with stakeholders. Describe to me one of your most innovative data visualization ideas that went beyond pie and bar charts.
  2. Can you describe how Data Analysis is used by businesses and other organizations?
  3. What is Data Cleansing and why is it important in Data Analysis?
  4. A a Data Scientist, how do you employ statistics to analyze data and develop business recommendations?
  5. Can you compare Sas, R and Python programming tools and describe their use in Data Analytics?
  6. Data Scientists do a lot of exploring and testing of hypotheses. Tell me about a time where you were given freedom to explore a business problem with very few parameters. What was your initial approach in attacking this project?
  7. Describe a project where you had a surprisingly difficult time dealing with unstructured data. How did you overcome the obstacles and what tools did you use?
  8. Many companies rely on Data Scientists to tell them what analysis is possible with the data available. Talk about a time when you took the initiative to recommend a new business measure for the company to track.
  9. How have past positions unrelated to data analysis helped you in your current profession as a Data Scientist?
  10. What experience do you have conducting text analytics?Describe a project you worked on that required text analytics.
  11. What statistical software programs do you have experience using in past positions in this field? Which one do have you the most experience with or feel the most confident using?
  12. In your past positions, have you had experience contributing to the improvement of data analysis processes, database management, data infrastructure, or anything along those lines? If so, please explain your contributions.
  13. Describe to me your experience with machine learning methods. Is there a particular method you have more experience with than others?
  14. Describe a time when you had to present findings/recommendations to a non-technical audience (very little or no background in data or databases). What strategies did you use to ensure the audience did not get confused and clearly understood the message?
  15. What data visualization tools do you have experience using? Which one is your favorite to use and why?
  16. Describe to me a data project you worked on in the past that you would do differently with the knowledge/experience you have acquired up to this point and/or new technology that was not available at the original time of the project.
  17. When your job requires you to be immersed in data, you can discover some interesting patterns or trends. What is the most interesting learning you discovered through the mining/exploration of data?
  18. The work of a Data Scientist can have a large impact on the strategy, and ultimately success, of a business. Is there a time you felt your work as a Data Scientist had a profound impact on strategy development? Explain your role and contribution.
  19. To be a successful Data Scientist, many in the industry believe it is important to keep up-to-date on the newest technologies and methodologies. What new data-related technology/methodology have you heard of that you wish you could learn more about?
  20. What programming languages do you have experience using? Of these, which do you have the most experience with? Which do you have the least experience with?
Answer Examples
1.
Data visualization is an important skill that will be used often when communicating results with stakeholders. Describe to me one of your most innovative data visualization ideas that went beyond pie and bar charts.
Data Science and analysis of complex data sets is a very technical discipline. However, the organization's stakeholders who use the results of the analysis need to be able to clearly understand what the data is telling them and be able to use it to improve their operations and help them make business decisions. You need to be able to present your work in a manner that is easy to understand and utilize. This is known as data visualization. The interviewer is seeking to understand how you organize and present the data to accomplish this objective.

William's Answer #1
"While the process of analyzing data is important, it is critical that the results of the analysis be useful to the stakeholders in the organization. It is important to understand what the stakeholder's objectives are when deciding how to present my results. One method I've found to be effective is to add graphs, pictures, and illustrations to my presentations and reports. Once, when presenting an analysis of customer usage trends for a product our organization sold, I incorporated images of the product and animated them to expand in size in relation to the growth in customer adoption, adding the statistics and actual growth numbers. The audience remarked about how clear this made the information."
William's Answer #2
"I am always cognizant of how my data analysis will be used by the stakeholders in our organization and seek to present my results in a manner that is relevant to the audience and in terms they can easily understand. I often use visual representations of the data along with the actual numbers to achieve this. Recently, when presenting a study on the implementation of process improvement and the results it generated, I used a photo of the actual production line, and animated it, increasing the speed of the line as the process was improved and the production times were reduced. I included the actual percentages of the time saved above the line so the management team could easily see the results which were achieved."
2.
Can you describe how Data Analysis is used by businesses and other organizations?
While this appears to be another Technical Question, it is actually more of a General Question. The interviewer is likely to ask this early in the interview to help establish a conversational tone for the interview and develop some avenues to follow up questions. As with any interview question, your answer should relate to the company's operations and how you believe they use data analytics to run their business. You can usually determine this from the information provided on their website and in the job posting.

William's Answer #1
"As a Data Scientist, I've come across many examples of how businesses use data analysis to improve the results of their operations. For example, eCommerce firms can use data analysis to understand customer behavior, reduce churn and better target their marketing. Financial organizations use it to evaluate investment opportunities and detect fraud. Healthcare companies employ data analysis to develop treatments for specific groups of patients."
William's Answer #2
"Data analytics is one of the most significant developments in making businesses and other organizations more efficient and effective. The insight data science provides helps virtually every organization to improve its operations through a better focus on outcomes and more targeted information used for intelligent decision making. Examples of this include search engines ranking pages depending on the specific interests of the user, and social media filtering information which the user is not interested in. Another use of data analytics is in robotics, which uses machine learning to handle new situations not previously encountered. Finally, businesses can extract information from large and unstructured sets of data which can then be used to develop products and target their marketing."
3.
What is Data Cleansing and why is it important in Data Analysis?
Technical questions like this one are straight forward ways for the interviewer to explore and confirm your technical competencies related to the position for which they are interviewing you. Your preparation for an interview should include researching and practicing technical questions, in addition to general and behavioral questions. Always answer technical questions succinctly, without embellishment or additional information.

William's Answer #1
"Data cleansing is the process of ensuring that data obtained from a wide variety of sources is suitable for analysis. It involves a high-level review of the data set, detection of any anomalies or inaccuracies, and correcting these to ensure the data is correct and accurate. It can also be used to eliminate components of the data that are irrelevant to the analysis being performed."
William's Answer #2
"It is always a good idea to cleans data before analyzing it. This involves reviewing the data for inaccuracies, irrelevant information or other items that will skew the analysis and result in conclusions that are incorrect or not usable. When performing a data cleansing operation, the Data Scientist looks for outliers or information that doesn't fit the pattern of the majority of the data. Inaccuracies are corrected and information not relevant to the analysis being performed is removed."
4.
A a Data Scientist, how do you employ statistics to analyze data and develop business recommendations?
Data Scientists use a variety of tools, statistics being one of the most used and commonly employed. An interviewer will ask this question early in the interview to set the stage, learn more about your skills and experience, and to guide you toward other, more specific questions. Keep this in mind when responding to this question, because it will provide you with the opportunity to move the interview in a direction that you are comfortable with and can easily address.

William's Answer #1
"Statistics is probably one of the strongest tools a Data Scientist has in their arsenal. It helps us to identify patterns, find hidden insights and quickly analyze large data sets. Statistics provide information about consumer behavior, interests, engagement and other aspects of the shopping and purchase process. They also allow for the quick development of models that validate assumptions and inferences."
William's Answer #2
"Of all the tools I use in the process of analyzing data, statistics is my favorite. This is the most mature methodology in the field of data science and there are a great many programs at our disposal. Statistical analysis is a straight forward way to identify trends, confirm a hypothesis, expose hidden insights and develop models business users need to make intelligent decisions. Statistics can be used to narrow the focus of an analysis and provide the users with the exact information they are looking for."
5.
Can you compare Sas, R and Python programming tools and describe their use in Data Analytics?
This is a Technical Question, which seeks to determine your technical capabilities and your knowledge of common tools used by Data Scientists. By specifying these tools, the interviewer is indicating that these are what their organization uses and expects you to be competent in. You should be able to compare these and state their purpose in analyzing data, even if you don't regularly use them.

William's Answer #1
"Sas, R, and Python are probably the most commonly used tools for data analytics. Sas has a wide array of functions, a user-friendly graphical interface, and strong reporting features. R's strength is that it is an open-sourced tool and is widely used in academic and research environments. Python is also an open-sourced product but is more widely used and supported. It is easy to learn and interfaces well with other tools. The best part about Python is its large portfolio of libraries and modules."
William's Answer #2
"While there are many data analytics tools available, Sas, R and Python are probably the most popular and widely used. Of these, I prefer Python. This is due to its large number of user-created libraries and modules, its ease of use, and its robustness in areas such as statistical operations and model building. R is also open-sourced but is more popular with the academic and scientific community. Sas is by far the most widely used data analytics tool and has an easy-to-use graphic interface and probably the strongest statistical functions. Sas' only drawback is its licensing cost, which can be prohibitive for smaller organizations."
6.
Data Scientists do a lot of exploring and testing of hypotheses. Tell me about a time where you were given freedom to explore a business problem with very few parameters. What was your initial approach in attacking this project?
This is another question that is meant to help the interviewer determine your level of creativity and initiative. These are qualities not typically associated with Data Scientists, but which are key to the results they produce. Organizations value employees that are willing to work independently with little supervision and are confident enough to be responsible for their actions. The best way to respond to this question is with confidence and a straightforward answer.

William's Answer #1
"One of the most rewarding parts of my job is the ability to work on a project with few parameters and the freedom to explore and experiment. I was recently assigned to determine why sales of one of our products had fallen off. I decided to approach the problem from two perspectives; those of the sales team and the customers. Using text analysis, data visualization, and other analytical techniques, then comparing the results from the two groups, I determined that the issue was one of communication. I recommended that the sales team modify their messaging and follow up with the customers in a more timely fashion. This helped to reverse the fall-off in sales."
William's Answer #2
"Many people believe that Data Scientists are very structured, disciplined individuals with little creativity or imagination. In my experience, just the opposite is true. Successful Data Scientists know that many times the answers they are seeking lay outside of the parameters which define the project they are working on. They realize that they need to be open to exploring new ideas without imposing any restrictions on their thinking or the analysis they perform. While working on a project to help improve the process for onboarding new customers, I was given a set of parameters for the project. Instead of limiting my analysis to these, I took the initiative to explore what other issues were involved with bringing a new customer on board. I discovered six other processes that were not part of the original plan for the project. After careful analysis, it turned out that two of these were critical. By adjusting and improving these, along with some other changes, the onboarding process was accelerated and customer retention was increased."
7.
Describe a project where you had a surprisingly difficult time dealing with unstructured data. How did you overcome the obstacles and what tools did you use?
By asking this question, the interviewer is revealing that their organization deals with unstructured data and needs to hire someone who has experience with this and can organize this type of data to make it usable. Your answer needs to include a specific example of how you accomplished this in a previous role to prove your ability to do it again.

William's Answer #1
"Unstructured data is difficult, but not impossible to work with while performing an analysis. The key is to utilize tools and techniques designed to effectively analyze this type of data. On a recent project, I was tasked with helping the sales team to improve its customer relationship management process. I utilized a combination of a NoSQL database and Amazon's Simple Storage Service (S3) to collect and analyze the data, producing the results the sales team needed."
William's Answer #2
"Unstructured data, also known as Big Data, is the fastest-growing type of data in the industry. This has presented Data Scientists with a challenge when attempting to use this type of data to analyze trends, processes and customer behavior. I have learned that special techniques and tools are required to incorporate unstructured data into the analysis I'm performing. These include looking for patterns, keywords, and sentiment in textual data, and using natural language processing technology. I recently worked on a project involving employee communications and attempting to improve individual productivity. Using the tools and techniques I previously mentioned, I was able to determine the specific behaviors the most productive employees exhibited. This enabled the management team to train and coach the other employees, thereby improving their productivity."
8.
Many companies rely on Data Scientists to tell them what analysis is possible with the data available. Talk about a time when you took the initiative to recommend a new business measure for the company to track.
Data Science by its nature is a disciplined practice with little opportunity for creativity or changes. However, organizations like their employees to be able to take the initiative and innovate to improve processes, with the goal of reducing costs or increasing the outcomes of actions they take. When preparing for an interview, you should have a few stories available which demonstrate initiative and out-of-the-box thinking.

William's Answer #1
"During one of my previous jobs I was on a team which focused on customer satisfaction scores. We determined this by analyzing data involving product returns, repeat purchases. customer referrals and by using text analysis. While performing this work I noticed a correlation between customer satisfaction and a specific feature of one of our products. I knew the feature couldn't be altered, but I thought that if it was highlighted in the product documentation and suggestions for its use emphasized. the customers would use it more often. I recommended this to the product manager who implemented my idea. The result was an increase in the use of the feature and a corresponding reduction in customer complaints."
William's Answer #2
"As a Data Scientist, my job is to analyze data sets and report the results. The other company stakeholders use this information to improve products, services, and processes or to make better business decisions. However, I like to look beyond the data and identify trends that are more subjective in nature. An example of this was when I was working on a project involving employee absenteeism at one of the company's manufacturing facilities. The data identified some factors contributing to the absenteeism patterns, but I sensed there was something else not indicated by our analysis. I took the initiative to informally interview a sample set of the employees and found that they had strong opinions about the food served in the cafeteria. I then added this factor to the study and confirmed that increases in absenteeism correlated with specific menu items."
9.
How have past positions unrelated to data analysis helped you in your current profession as a Data Scientist?
The purpose of this question is to gain a broader picture of your background and experience. In addition to being able to perform the tasks related to the job you are interviewing for, organizations prefer to hire individuals who can expand the role of the job to accomplish other organizational objectives. They are also interested in your fit and how you may improve the company culture.

William's Answer #1
"When I was young and still in school, I didn't have the goal of becoming a data scientist. However, I was naturally curious and enjoyed learning new things. I also liked solving problems, especially using numbers and sets of data. One of my first jobs was in a library where I had to reshelve books. I quickly learned that if I spent some time organizing the books before I began replacing them on the shelves, I could reduce the amount of time the job took. I challenged myself to continually beat my previous times by devising new ways to organize the books and navigate through the library. I tracked this and charted my progress. This made a routine job more interesting and enjoyable."
William's Answer #2
"Data Science involves identifying an issue, determining a methodology to analyze it, collecting data, performing the analysis and interpreting the results. My first experience with this type of process was while working in a grocery store during high school. My job was to replace items that customers either returned or decided not to purchase while they were checking out. At first, I considered this to be a boring and repetitive job, but then I started to notice patterns in the products I was working with. I began keeping track of the products and looking for patterns. I found that many of the products not purchased or returned by customers were ones that had ambiguous pricing information. I shared my charts and conclusions with the store manager, who agreed with my results and took steps to clarify the pricing information. I continued to track the products and found that the changes we made resulted in fewer returns and non-purchases." This sparked my interest in data science and led me to this career."
10.
What experience do you have conducting text analytics?Describe a project you worked on that required text analytics.
This is another technical question which the interviewer will ask in order to confirm your skills and experience as a Data Scientist. They want to ensure that you are qualified for the job and are familiar with a specific process that they use to analyze data to improve the results of their operations.

William's Answer #1
"Text Analytics is the process of creating meaning out of written communications. A common usage of this is In a customer experience context, examining text that was written by, to, or about customers. This helps find patterns and topics of interest and then enables the organization to take action based on what it learns. While working on a recent project involving the review of a service our organization provides, I examined the email communications between our support team and the customers. My analysis identified a specific issue that customers inquired about frequently. We then reviewed the documentation related to this and realized that it was vague and somewhat confusing. After updating the information and performing subsequent text analysis, I confirmed that the number of customer inquiries about this issue had dropped by 70%."
William's Answer #2
"Text analysis is the process of examining written communications to discover trends or create meaning. It involves sentiment analysis, key phrase extraction, and entity recognition. It usually is used with customer communications to improve a product or service. While working on a recent project to determine which of our company's services customers found the easiest to use, I analyzed email exchanges between the sales team and their customers. The results revealed that while customer surveys indicated that one service was the most popular, the email exchanges found that the customers actually enjoyed using another service more. This enabled the company to adopt some of the features of this service to improve customer satisfaction with the other ones we offered."
11.
What statistical software programs do you have experience using in past positions in this field? Which one do have you the most experience with or feel the most confident using?
This is a technical question, the purpose of which is to determine your familiarity with and knowledge of software used by data scientists. The interviewer is also interested in learning whether you are adept at working with the software and tools their organization utilizes. The best way to answer this question is to first state the names of the software you have used and are familiar with, and then make the point that most statistical analysis software products have similar features and that you've been able to easily transition from one to another one when necessary.

William's Answer #1
"In my current position, we use Tableau for the majority of our work. We also have licenses for Statgraphics and JMP Statistical Software, but these are only used in circumstances in which their unique features are more suited for the task at hand. I've also used Salesforce Analytics Cloud and MATLAB in previous roles. I've found that transitioning to a new software analytics tool is relatively easy due to the similarity in the features and user interface between the different packages."
William's Answer #2
"The primary software tools I use for statistical analysis are Tableau, Statgraphics, and JMP. Each of these performs similar functions, but each also offers unique features that make specific types of analysis easier and more accurate. My experience has taught me that once you are familiar with the basic functions of statistical analysis software, you can move between different tools by simply learning the user interface and the functions each one offers. I'm curious about which tools your organization prefers and why."
12.
In your past positions, have you had experience contributing to the improvement of data analysis processes, database management, data infrastructure, or anything along those lines? If so, please explain your contributions.
Keep in mind that organizations hire people to help them achieve their objectives. This question is seeking to learn about the past contributions you made in your previous positions and to explore whether these are similar to the challenges their company is facing. You should understand the issues the company routinely faces from your pre-interview research. Make sure your answer to this question aligns with the needs of the employer.
William's Answer #1
"In my most recent role, I introduced my team to new data visualization techniques to enable us to perform the data analysis faster and more accurately. It also enabled us to communicate our findings to the other business stakeholders in a manner which they could easily understand and relate to."
William's Answer #2
"One of the key objectives for any job I have had is continuous process improvement. Early on I realized that investing some of my time in pursuit of making the work I did more productive would benefit both me and the organization I worked for. An example of this was while working with my previous employer. After learning how they currently collected, processed, cleaned and analyzed their data, I researched different tools and methodologies that would improve the process. I developed a set of recommendations and presented them to senior management. They agreed with my assessment and moved forward to implement my suggestions. As a result, the time to complete an average data analysis project was reduced by 40% and the results were more accurate."
13.
Describe to me your experience with machine learning methods. Is there a particular method you have more experience with than others?
The purpose of this question is to explore your knowledge and experience with machine learning. The interviewer may want to confirm not only your skills in this area but also your direct knowledge of the machine learning methodologies their company utilizes. Be prepared to provide a concrete example and the rationale behind using the methodologies you chose.

William's Answer #1
"Much of my experience with machine learning is in the area of medical imaging. Our team employed machine learning methodologies including classification, clustering, and regression analysis to help improve the accuracy of the assessment of the images our equipment produced."
William's Answer #2
"Since most of my work was focused on filtering spam within the email application we developed, I utilized the Classification methodology of machine learning, specifically the NaiveBayes algorithm. This allowed us to address the large data set we used for training and the multiple attributes we filtered for. "
14.
Describe a time when you had to present findings/recommendations to a non-technical audience (very little or no background in data or databases). What strategies did you use to ensure the audience did not get confused and clearly understood the message?
This is another example of a Behavioral Question and you can use the STAR framework to organize your answer. The interviewer is interested in learning about your communication skills and style. Walk them through your answer systematically, describing how you communicate complex issues in a clear and non-technical manner.

William's Answer #1
"While Data Science is a complex and highly technical field, the people who use the information I provide are usually experts in fields other than data. Therefore, when I present my findings, I work hard to communicate them in terminology the audience is familiar with, focusing on the conclusions and recommendations rather than the data, statistics, and analysis methodology. This approach results in the organization attaining the business objective of the analysis. I also prepare myself to answer questions about the science behind the analysis if necessary."
William's Answer #2
"It is easy to get distracted by the nuances of data analysis when presenting findings of a project I've worked on to the decision-makers from other parts of the organization. This is because I'm most comfortable with the data and the analysis and may not be familiar with the other operational aspects of the company. When preparing to present to a non-technical audience, I take the time to understand how my findings and recommendations will be used, the priorities of my listeners, and the desired outcome of the meeting. I then focus on communicating clearly, and concisely, using terminology I know the audience is familiar with and understands."
15.
What data visualization tools do you have experience using? Which one is your favorite to use and why?
This question is similar to the one about statistical software programs in that it is attempting to discover your technical knowledge and your familiarity with the tools the company you are interviewing with uses. Again, the best response is a direct one, stating your knowledge of software tools, your preference and the reason for your opinions.

William's Answer #1
"Data visualization tools I use include Google Charts, Tableau, Grafana, Chartist.js, FusionCharts, Datawrapper, Infogram, ChartBlocks, and D3.js. I prefer Tableau because it offers a variety of visualization styles, is easy to use, and can handle large data sets. The other reason I like Tableau is that their help desk is very responsive and open to suggestions from the user community. It isn't a true open-source software, but the product is continuously being improved by the developers and the company's customers."
William's Answer #2
"There are many data visualization tools available and I've used quite a few of them. They include Google Charts, Tableau, Grafana, Chartist.js, FusionCharts, Datawrapper, Infogram, ChartBlocks, and D3.js. My favorite is Google Charts. This is because it is easy to use, robust, widely accepted and the licensing fees are low. Another reason I prefer Google Charts is that it can be customized to make it better suited to the specific project I'm working on. Since most of the work I do is similar, once I have tailored the tool to my needs I don't have to reconfigure it each time I use it."
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