Overview of the Data Science Hiring Process

What do you go through during the data science hiring process?

Once you make it to the interview stage for a data science position, what can you expect? The interview process will typically consist of four rounds starting with the behavioral interview and then a technical interview followed a take-home assignment and finally a second technical interview. This might vary from one company to another, however, this is generally the followed scheme in the hiring process. In this article I will go through each stage of the hiring process and what to expect in each of them and briefly how to properly prepare for each of them.

In previous articles, I discussed how to reach the interview process and pass the application screening process. You can find tips on how to improve your resume and Portofino to pass the screening process:

Another way to pass the screening process is through internal referrals, you can find more information about getting internal referrals in this article:

Table of contents:

1. Behavioral Interview
2. Technical Interview 2.1. SQL Questions 2.2. Python Programming Questions 2.3. Probability and Statistics Questions 2.4. Machine Learning Questions 2.5. Product Case Questions 2.6. Resume-Based Questions
3. Take-home Data Science Assignment
4. Technical Interview With Your Team
5. References

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1. Behavioral Interview

The first type of interview we will discuss is the Behavior and Experience interview. In this interview, you will be asked about what you would do in hypothetical situations and also about how you have worked with others in the past. You will also be asked about your previous teams as well as your previous projects. They’re going to make sure that you fit with the common company's culture. As a candidate, it is crucial that you don’t overlook this interview thinking that they are not as important. They are most certainly important. Big companies such as Google and Amazon are putting more emphasis on these interviews.

Here are some example questions:

• Can you talk about a previous data science project you have done?
• What problems have you solved at your current or previous job and how did they contribute to the overall success of the coming?
• Can you tell me a time you went above and beyond your responsibilities?
• What are some of the difficulties you have faced in the past with your work? How did you resolve them?

If you want to learn more about behavior interviews, I am working on an article that will cover all aspects of a behavioral interview, including how to present past projects and dos and don’ts in a behavior interview.

2. Technical Interview

The second interview after the behavioral interview is a technical interview which is always with the team leader or hiring manager. It is important to remember that the type of position you target will affect the questions you need to focus on. For analytics-driven roles, you should prioritize the product case and the SQL questions and for algorithm-driven roles, you should prioritize the machine learning and deep learning conceptual and practical questions. For both, you should have strong python coding skills.

2.1. SQL Questions

The first type of questions to expect in a technical interview is SQL questions. These interview questions are critical as SQL is used very often in the daily job of a data scientist. Whether you work for a small or big company, you can use this tool consistently to do data analysis, diagnose issues and get insights from data. Therefore, you must demonstrate proficiency in this interview. The SQL interview questions will test your familiarity with SQL language, syntax, and function. In addition to this, it will also test the ability to logically understand the problem and create efficient queries in a short amount of time. The questions cover things from filtering data and joining different tables to computing complex business metrics on users, activities, or system logs. You can find many sample questions on leetcode and hacker rank. Also, you can check my blog on SQL and DB interview questions for data scientists.

2.2. Python Programming Questions

The next type of interview questions is the coding and python questions. These questions test your coding ability, which includes proficiency in programming or scripting languages, especially python. Additionally, you will need to show that you present computer science fundamentals, including an understanding of common-use algorithms and data structures.

The coding questions refer to coding up algorithms like binary search, quick select, and these structures like list, step, Q, tree and graph, et cetera. The questions vary from implementing a simple algorithm such as a Quick Select to solving a more complicated problem involving using a search algorithm such as BFS or DFS on a tree or a graph data structure.

For these questions, you are expected to have a logical understanding of the problems and be able to come up with efficient solutions within a limited amount of time.

]You can practice these questions on Leetcode where you can find many examples. So if you’re looking for a data scientist role in the algorithm-driven track, then the Coding questions will frequently appear in the interview process. You can also find interesting python interview questions for data scientist positions in this blog:

2.3. Probability and Statistics Questions

The third technical questions to expect in the technical interview are probability and statistics questions. These questions will test your knowledge of applied statistics and probability. Applied statistics is particularly important for a data scientist. It helps you with leading A/B testing, data analysis, and making data-driven decisions. Thus, these questions are where you must demonstrate that you have the technical skills necessary to perform the job.

The question in this type of interview can be conceptual such as:

• what is P-value? How do you calculate Khan’s interval? Can you explain the central limit theorem?
• What are the assumptions of linear regression?

They can also involve calculations such as:

• what is the probability of obtaining two consecutive paths from five-point tosses?
• What’s the probability of winning a game given some specific conditions?

If you are looking for a position in the analytics-driven track, then the three types of interviews. Here are statistics and probability interview questions that are expected to be asked in a data science interview:

2.4. Machine Learning Questions

We have the Machine learning interview. This interview is closely related to the responsibilities of an algorithm-driven position. You must demonstrate your knowledge of the basics of machine learning. The questions will look at things like how machine learning models work, the pros and cons of different models, techniques that are applied when dealing with different data sets, and tuning parameters.

Example questions for this type of interview include:

• what is the overfitting problem and How do you deal with it?
• How do you deal with an imbalanced data set?
• What does the random in a random forest model mean?
• What are some metrics to evaluate a classification model?

2.5. Product Case Questions

The final type of technical interview questions is the product case questions, which are also sometimes called Business case questions. The reason that companies have this type of question is that they want to evaluate your ability to measure and your knowledge of products and A/B testing which are core skills of data scientists, especially analytics-driven data scientists.

So the product interview is designed to test your product knowledge and critical thinking skills. In a product case interview, typically you are given a business scenario or problem and then ask to explain your approach to solving the problem and make suggestions. The questions can vary quite a bit, but they may include things like designing experiments and making decisions about whether to launch a product or not.

Some example interview questions are:

• Can you provide some metrics for major user engagement?
• What are the pros and cons of using them?
• What are the things to consider when designing an experiment?

To do well in this interview, you will need to demonstrate an ability to design metrics, diagnose Magic Shift, and have ideas about how to improve a product and solve business problems.

2.6. Resume-Based Questions

The last type of questions that you should expect to be asked are technical questions that are based on your resume and previous projects. You would be to explain some of your projects and then the interviewer will further discuss more details about the projects from a technical point of view.

3. Take-home Data Science Assignment

This is the third round of the data science hiring process is the taking-home test or assignment. This is a very common step in the data science hiring process. In this step, they usually give you a broader business statement to tackle and an accompanying data set to use to solve the problem. This business problem is usually related to the company domain and related to the case studies they are currently working on. These tasks can be completed anywhere between one hour to a couple of days depending on the required tasks and the complexity of the dataset.

This interview step usually evaluates the following skills:

• Problem-solving skills: This is one of the key elements to be evaluated in this interview step. The evaluator wants to see your ability to comprehend the problem statement, break it down into smaller tasks, develop solutions for them, and discuss potential solutions.
• Code-writing skills: These exams are designed to test whether you have the basic code-writing skills expected of a data scientist.
• Data Science Skills: You will be required to do several data science tasks starting from importing and reading the data, cleaning the data, exploring the data, feature engineering, modeling the data, and visualizing the results. You might not have to do all of them or apply just simple tasks in each of them.
• Speed: In an online exam where tracking of completion time is possible, you may also be evaluated on the speed with which you can code.

You can prepare for this interview you can:

• Create an often-used code library: You can build out basic functions in a programming language of your choice, so you have them handy when you have to work on a take-home project. Here are a few functions you can consider building out: 1. Impute missing values 2. Plots to do exploratory data analysis 3. Train and score a classification and regression model
• Practice coding exercises: You can practice coding challenges on online platforms like CoderByte, HackerRank, LeetCode, and CodeSignal. Most of these online platforms offer free exercises for you to practice.
• Create Tasks-Based Projects: You can create several projects on small datasets. These projects should cover the basic machine learning tasks: regression, classification, and regression.

4. Technical Interview With Your Team

The last stage of the data science hiring process usually is a technical interview with the team members you are joining. In this interview, first, the taking-home assignment will be discussed with you. You are expected to have an excellent understanding of the task details and the output of the task. You should be able to answer why you have taken certain decisions in the project. You are expected to be asked similar questions:

• Why have you chosen this model?
• Why you chose this evaluation criteria?
• What do these results mean?
• How did you handle the missing data and why you chose this way?

In this interview, you should expect to be asked more questions about the case studies the team is currently working on so you should do prior research and prepare to answer a technical question related to it. Finally, this interview also measures whether you will fit with the team and will be able to work with them and communicate well with them or not. Therefore it will be good if you can know more about the team member beforehand so as to be able to have better communication with them.

5. References

1. 6 Types of Interviews to Get a Data Scientist Job
2. How to ace the take-home data science assignment?

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