You might enjoy data science if you’re familiar with systematic sampling, multivariate analysis, and reinforcement learning. However comfortable you may feel with these quantitative topics, a data scientist interview is still nerve-wracking. To help you land your dream data science job, below are the top data scientist interview questions and answers.
No matter what statistical technique you’re best at, you should prepare for as many interview questions as you can. Refresh your memory on things like the linear regression model, activation function, and bivariate analysis. Identify your weaknesses and practice your answers to questions in simple terms. Read on to learn more about data scientist job interviews.
What Is a Data Scientist?
A data scientist is an analyst who cleanses, organizes, and interprets unstructured and structured data so businesses can make strategic decisions. You will often apply concepts like linear regression, deep learning, machine learning, root cause analysis, linear combination, and probability sampling to your projects.
If building a complex statistical model and conducting randomized experiments sounds exciting to you, then a data science career is a great choice. According to ZipRecruiter, the average salary for data scientists is $119,413, which is extremely high. Data scientists with skills in DBSCAN clustering and SQL queries can earn even more.
Answers to the Most Common Data Scientist Interview Questions
A hiring manager’s questions during a data scientist interview depend on the company you’re applying to. However, you can generally prepare for what to expect at your interview. There are common questions asked in a data science interview, like behavioral, technical, and general data science interview questions.
To successfully answer any data scientist interview question, you must understand how to implement specific techniques and solve false positives. You should also know how to work with predictive power, outlier values, systematic sampling, and data visualization. Content-based filtering and the binary classification algorithm are also important to know.
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Top Five Technical Data Scientist Interview Questions and Answers
Technical questions for data science interviews determine your abilities to work with practical concepts like logistic regression, independent variables, decision trees, and probability sampling. You may also come across data modeling questions. Below are the top technical questions for a data scientist interview.
How would you explain the difference between a histogram and a box plot?
If you want to become a data scientist, prepare to work with histograms and box plots often. Hiring managers need to know you can differentiate between these two data visualizations. So, when answering this data science interview question, go in-depth about the differences between these two data visualizations and how data scientists use them.
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Histograms are bar charts whereas box plots aren’t. The former demonstrates the frequency of numerical variables values while the latter presents data distribution. Histograms estimate the given values probability distribution, and the box plot is used to evaluate range, outliers, and quartiles to compare multiple charts simultaneously.
What are the different features between supervised and unsupervised learning?
Machine learning is a vital part of data science. Hiring managers ask these questions to evaluate how well you know machine learning for data science. You want to be very detailed in your answer to this question and specify all the differences between supervised and unsupervised learning.
For supervised learning, the input is known and labeled data, and there is a feedback component present. We usually use supervised learning for logistic regression and decision trees. Unsupervised learning functions on unlabeled data, and there is no feedback component. We use this for hierarchical clustering and k-means clustering.
What does the term “confusion matrix” refer to?
Statistical techniques are a dominant practice in data science, and this is where the confusion matrix becomes relevant. By defining a confusion matrix, you can confirm you know how to evaluate the performance of a classification model. In turn, you have a firm handle on statistics and probability. Don’t confuse this concept with correlation or covariance matrix.
A confusion matrix is a system that summarises the number of incorrect and correct predictions, including count values. We break down these predictions according to class. By these results, you’ll be able to determine how well your classification model performed against actual target values.
What are the steps of creating a decision tree?
In a career like data science, you need to know how to make a strategic decision. Because of this, the hiring manager will ask these types of questions regarding decision trees. Answering this question reflects your ability to organize data and develop a successful analysis using accurate insights. Below reflects how you can describe the steps to create a decision tree.
- Determine the data classes that will be the basis for the tree.
- Refer to the “Play Golf” column and calculate the Entropy for the classes.
- After each split in the decision tree, calculate the Entropy for each attribute.
- For each attribute, calculate the information gain. To do this, use this formula Gain(S,T) = Entropy(S) – Entropy(S,T). Use the attribute with the largest information gain for the split.
- Execute the first split in the decision tree based on the attribute with the most extensive information gain from Step 4.
What are the cons of a linear model?
This question determines if you understand the risks of working with a linear model. Your knowledge will also show that you have the skills to differentiate between machine learning models so you can identify weak models and use appropriate models for your project. When you answer this question, ensure to list as many drawbacks of a linear model as possible.
When working with a linear model, you’re limited to working with linear relationships which are not correct for every dataset. Linear progression prevents you from look at the extreme values of a dataset as you can only view the mean of a dependent variable and independent variables. Your data must also be dependent when working with linear regression.
Top Five Behavioral Data Scientist Interview Questions and Answers
A behavioral data scientist interview question is to evaluate your personality traits and how you approach situations. While your technical knowledge doesn’t matter much for these questions, you still need to prepare for them. The Bureau of Labor Statistics suggests that you should prepare for every question and give specific answers.
What are the values of a good data scientist?
A hiring manager will ask you this question to determine your professional qualities and your aspirations. Your answer will also reveal your perspective on the best way to do your job. Be honest and talk about how your own personal values reflect those of a good data scientist.
In general, data scientists should have excellent time management skills and take charge in stressful situations. Professionals should also pay attention to detail for any dataset they work on. They need to understand business requirements and determine how they can make a genuine impact on the company.
What type of work environment do you thrive in?
This question is tricky because, although the hiring manager would want your honesty, they’re trying to gauge if you’d be a good fit for their company. Research employee reviews on a site like Glassdoor to get an idea of the expected working environment. If it’s a compatible culture for you, base your answer on the features you enjoy about that environment.
For example, if the company has a slower-paced work environment, you can say that you enjoy working in an environment that’s not too overwhelming but does challenge you. On the other hand, if the work environment is fast, explain that you love working in an environment that’s constantly evolving and presenting new problems to solve.
How do you plan to add value to the team?
When you’re performing data analysis, data validation, making decision trees, and using systematic sampling, you’ll rely heavily on feedback from your colleagues. Consequently, hiring managers want to understand how you plan to help and optimize your team. Your answer will say a lot about your teamwork and communication skills.
To answer this question, you should play to your strengths. You can call back to previous answers about your work experience or skills. For example, you can say that you want to offer a fresh, innovative perspective and strive to help increase efficiency, effectiveness, and accuracy in your projects.
What are your biggest weaknesses?
Nobody wants to admit they have weaknesses and faults. Yet, employers need to know this to understand how you plan to improve your weaknesses. Whether you struggle with cross-validation, deep learning models, or translating complex functions, be honest about your weaknesses and how you plan to change them.
No matter what your weaknesses are, you should take ownership and explain your motivations to change them. For example, if you have poor time management skills, you can try keeping an agenda and setting alarms to hold yourself accountable. If you struggle with the random forest model or logistic regression model, mention those too.
How do you stay updated with data science trends?
There might be a better way to approach a clustering technique, implement deep learning models, or create machine learning algorithms. Because of this, you need to stay ahead of data science trends. Show how you’ll accomplish this to prove you’re passionate about your job and keeping your statistical processes evergreen.
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A good answer would be to say that you stay updated with data science trends through an online data science community. Mention different blogs, podcasts, and other resources that you interact with. You can also take online data science courses throughout your career and describe your experience to your interviewer.
Top Five General Data Scientist Interview Questions and Answers
General data science interview questions determine your level of data science expertise and how well you know essential tools. These types of questions can also evaluate your passion and dedication to the data science industry and how you plan to progress within it. Below are the top general data scientist interview questions.
Which data scientists do you admire and why?
Keep in mind that there might not be a singular correct answer to this data science interview question. This question is typically asked in interviews to evaluate your career ambitions and values. You can mention industry experts like Jeremy Stanley and Monica Rogati. Be sure to explain why you look up to them specifically.
If you already have a connected network in the industry, now is the time to do so. For example, if an old coworker taught you how to use batch gradient descent, you can explain its impact on you and mention their name. This is a great way to make your interview helpful in the long run.
Which three biases occur in the sampling process?
When working with data samples, you need to identify sampling biases to ensure accurate statistical results. A biased sample can include skewed distribution or an incorrect sample size. It depends on batch size and how well populations are represented within the data. However, when you do, your employer needs to know you can deal with the bias.
The three sampling biases are undercoverage bias, selection bias, and survivorship bias. Survivorship bias occurs when we focus only on existing data and not data that could have existed. Selection bias is when the wrong things or people have been selected for analysis. Undercoverage bias happens when an element of the population is not considered.
Why is resampling necessary sometimes?
You might have to perform some resampling while working with data. The hiring manager asks this question to test whether you’ll know when resampling should occur. Without this knowledge, your outcome could be a false negative or false positive.
These cases include needing to validate data through random subsets or substituting data labels during tests. Sampling bias can add huge problems to the process and affect an entire dataset. A randomized experiment might have better outcomes than one that has too much going on.
Why do we perform A/B testing?
This data scientist interview question is crucial as it ensures you’re using accurate statistical strategies. Your potential employer will want a professional who uses the best mathematical processes, making your answer to these types of questions particularly crucial. A/B testing is when you test two variables, A and B, against each other to determine the best strategy.
What is cross-validation?
Statistics are at the core of data science, and hiring managers want to evaluate if you can perform statistical analysis correctly. If you don’t know how to perform cross-validation, you can’t successfully compare and assess data.
To explain cross-validation, you might explain that cross-validation is a statistical method where you break up data into segments for comparison. One segment is used as a training dataset to learn a model, and the second is used to validate the model.
Tips to Prepare for a Data Scientist Interview
1. Review Your Resume
Ensure your resume has all the soft skills and technical skills you need for your data science interview. Although you might know binary classification, root cause analysis, and how to work with extreme values, certain employers may want particular skills. If needed, consider doing data science projects for beginners to buff up your skills beforehand.
2. Find Out the Structure of the Interview
Some data science job interviews require you to complete a technical assessment. This information is worth finding out so you can appropriately prepare for a technical interview. Reach out to the company and ask if the interview contains a technical assessment portion. This will help you determine how to prepare and what you need to practice.
3. Thoroughly Research the Company
Conduct research on the company you’re interviewing for so you can determine how to approach your interview. During your interview, it’s best to mention personal details about the company, including its past projects, an article on its blog you enjoyed, or perhaps its podcast or YouTube channel. Mentioning these facts will show you have a genuine interest in the company.
What Skills Should I Put on My Data Science Resume?
The skills you should add to your data science resume include all programming languages you’ve mastered, machine learning and AI experience, and any relevant statistics courses. Let’s take a closer look at why these skills are vital for a data science resume.
Employers expect you to learn Python, SQL, Java, R, and other programming languages for data science. Add the coding languages you’ve perfected on your resume, as these are essential skills to fulfill data science duties. For an extra touch, order the coding languages according to your proficiency, starting with those you have the most experience in.
Artificial Intelligence and Machine Learning
AI and machine learning are vital for working with artificial neural networks, determining actual and random values, and optimizing various data science processes. Machine learning focuses on statistical models and gradient descent to draw accurate insights, and AI bases its strategies on decision trees. Deep learning is also a good topic to mention.
Probability and Statistics
Working on large datasets ultimately relies on probability and statistics. You need to know how to identify sample sizes, actual and outlier values, and bivariate analyses. It’s best to add all relevant probability and statistical skills to your resume. To boost your resume, add examples of statistics or science projects you’ve worked on before.
How to Find Data Scientist Jobs
When your various statistical skills like binary classification and deep learning are up to par, you can start looking for data science jobs. Resources like ai-jobs, DataJobs, and icrunchdata can help you find the perfect data science career. Below are the top ways to find a data science job explained.
This board is excellent if you’re looking for data scientists job postings to work with non-gaussian distribution, deep learning, and neural networks. You can find senior data scientist positions, entry-level roles, and machine learning career opportunities. There are also insights for more information about particular data science topics.
This job board provides career listings for data science, data analytics, and data engineering jobs. You will also find resources on data science careers and big data. Using DataJobs, you can find a data scientist vacancy in no time and start working with things like probability sampling and clustering algorithms.
icrunchdata is a central hub for data science job postings. To find available careers, you can filter the results by skills to find a job that suits your expertise. If you’re excited to jump into creating a machine learning model, conducting random sampling and univariate analysis, or developing a simple learning algorithm, search for opportunities on icrunchdata.
Data Scientist Interview Questions FAQ
The skills a data scientist must know include probability and statistics, artificial intelligence and machine learning, and programming languages for data science. This background of expertise will help you ace a data science interview. You can also learn more advanced topics like collaborative filtering and dimensionality reduction.
To prepare for a data science interview, thoroughly review your resume and ensure your soft and technical skills are up to par. You should also determine the structure of the data science interview and research the company you’re applying to.
You can expect behavioral, technical, and general data science questions. In some cases, employers may also want you to complete a technical assessment to prove your data science skills. This could include normal distribution, random forest techniques, linear transformation, and predictive models.
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