For data scientists, the leap to data science management brings on a number of changes.
Your day-to-day function shifts to data science leadership and management tasks and away from individual contributor-type work (although you’ll still do some of that). Similarly, the interview process is much different.
Although you can still expect technical data science interview questions in manager interviews, the main focus will be on behavioral, leadership, and management questions.
In other words, data science manager interview questions are much more discussion-based, and they require candidates to craft STAR framework stories to help interviewers understand their accomplishments, management style, and leadership abilities.
Need some help practicing for an interview? We’ve compiled a list of the most common types of data science manager interview questions, with examples for each.
Data science management interviews focus more on behavioral questions, especially around leadership, problem solving, product management, and team management. Although technical questions are asked such as (SQL, Python, and machine learning), these interviews are much more discussion-based and assess your ability to lead, execute, and manage a team.
Data science manager interviews typically ask questions related to the four core functions of the role. Data science managers are responsible for:
Day-to-Day Operations - Managers oversee the data science operation. Specifically, they’re responsible for budgeting, assigning tasks, hiring, team development, and monitoring performance.
Setting KPIs and Goals - Managers create the vision for the data science team. They’re responsible for creating the roadmap, defining success, and providing direction so that the team can achieve goals.
Cross-Functional Collaboration - Data managers collaborate with department heads and C-Suite executives. Ultimately, it’s the manager’s responsibility to align the work of the department with the company’s KPIs.
Overseeing Data Science Output - Managers have a similar role to project managers; they’re responsible for shepherding data science projects from initial conception into production.
In addition, interviews for data science management roles assess critical management skills:
Leadership - You must be strong at leading not just a team, but also setting a vision for the department, and executing it. Leadership questions in data management interviews tend to look at past projects and accomplishments or ask how you would approach certain ideas and situations.
Communication (Behavioral) - Managers in any industry must be strong communicators. They should be adept at using communication to motivate and inspire, gather information, and provide insights to non-technical audiences.
Problem Solving (Behavioral) - Managers must be able to quickly extinguish problems as they arise. For example, an effective manager can find creative solutions for coworker disputes and low team morale, accomplish goals with limited resources, maintain tight deadlines, and bringing organization to department operations.
Business / Product Sense - Managers should understand how to scale the output of a data science team to impact the organization.
Although the focus of interviews is behavioral and leadership questions, there will be technical data science rounds as well. You can’t be a data science manager without solid data science skills. Generally, data science managers get asked intermediate SQL, Python, and machine learning questions. Your interview prep should include a variety of practice problems in these categories.
For more technical questions, see our guide: 100+ Data Science Interview Questions.
Watch a video overview of the types of questions that get asked in data science manager interviews:
Data science manager interviews are similar to interviews for individual contributors. These interviews start with 3 initial rounds:
Recruiter Call - A short call with the recruiter to learn more about the position and interview process.
Hiring Manager Screen - A one-on-one call with the hiring manager to learn more about the role. You’ll also likely be asked about your resume, previous positions, and other specific questions to determine if you’d be a good fit for the role.
Technical Screen - Manager positions generally require an intensive SQL case study question. These questions usually contain multiple parts. You start by proposing a metric, write the SQL code to produce that metric, and then use the metric you’ve pulled to investigate a problem.
If you pass the initial screening, you’ll be invited to an onsite interview. Onsite interviews for data science manager positions include 5 one-on-one rounds. However, only one round is typically technical, while the other rounds are behavioral (2) and leadership (2) focused.
Here’s what to expect in on-site interviews:
Technical Round- In general, technical rounds for manager interviews typically focus on case study questions. However, on-site rounds usually don’t require coding, and generally focus on product/business cases or machine learning cases.
Leadership Rounds - Expect two leadership rounds. Leadership rounds assess your management style, your ability to lead and inspire a team, and your communication skills. These questions are usually discussion-based. However, there may be elements of case questions incorporated, e.g. here’s a scenario, how would you approach it.
Behavioral Rounds - Behavioral questions in data manager interviews typically assess the impact, deliverables, and challenges you’ve experienced in previous roles.
See our guide How to Study for Data Science Interviews for ideas on how to approach interview prep.
Behavioral interview questions are open-ended discussion questions that are used to learn more about your professional experiences, management style, and communication skills. These are similar to behavioral questions in traditional data science interviews.
However, a key difference is that your answers should align with the management position.
For example, say you were asked: “Tell us about a time that you failed at work.” You don’t want to talk about an entry-level error you made – you should be choosing a mistake that actually impacted the organization.
One of the best ways to approach behavioral questions is to use a framework like STAR. Frameworks provide structure to an answer and transform them into stories with a beginning, middle, and end.
With STAR, you highlight the Situation, Task, Action, and Results. Therefore, for the above question about failure, you would describe the situation and task: “I missed a critical deadline, and we had to move much faster to push the product to production.”
Then, you talk about the actions you took and the results: “I held an all-hands meeting with the data science team and provided a schedule for how we would finish the update as soon as possible. I jumped in and provided coding support as well. As a result, we finished the project three days behind schedule, but it had minimal impact on customers.’
Ultimately, you should create a list of potential behavioral questions with framework answers for each question.
Quick Tip: Avoid being too thorough in your answers. Instead, leave some room for interviewers to follow up and learn more about the situation.
Question: Tell Me About a Time That You Missed a Deadline?
Answer: Hint: Remember S(ituation), T(ask), A(ctions), R(results).
“I was in charge of a new feature launch, which we wanted to launch by the end of the month. However, we ran into technical difficulties, and it became clear we would miss the deadline. I held an emergency meeting with the team and created a roadmap for completing the project ASAP. I also communicated cross-functionally with the affected teams. After introducing the roadmap, we worked at an accelerated pace to develop the feature and were able to complete the project four days after the deadline. The feature went into production and it was a successful launch. Because of the communication, we were able to sync with marketing and product operations, so that everything ran smoothly.”
More Example Questions
The product sense questions that data science managers face fall into two categories: general product sense questions (similar to what you’d expect in traditional DS interviews) and product leadership questions.
General product sense - These questions assess your understanding of how data science can impact a business. General product questions, like technical questions, are used to assess your intuition and skills, rather than your management style.
Product leadership questions - Leadership questions are used to determine how you would scale data science for the organization. These are generally high-level questions based on goal setting, measuring, project management, and maximizing the output of the data science team.
One tip: slow down with your answers. Always ask a clarifying question, even if you’re just restating the question. These questions help you gather more information to provide a more insightful answer. Rushing into answers is a red flag in managerial interviews.
Question: DoorDash is launching in New York City and Charlotte and needs a process for selecting delivery drivers. How would you choose drivers for these deliveries? Would the selection criteria be the same for both cities?
Answer: See a full solution to this question on Interview Query.
“First, the criteria wouldn’t be the same. NYC and Charlotte are very different cities. Many NYC dashers ride a bike, whereas, in Charlotte, they’re much more likely to drive a car. Similarly, density affects traffic patterns (a key factor in determining an acceptable delivery range).
Ultimately, you could determine the best dashers algorithmically. If we examine the inputs and outputs, we can create a model based on either historical data from comparable cities, or create a heuristic that can be constantly tuned as launch progresses. For example, in choosing a Dasher for a particular order, we have feature input such as:
We can measure these against our output variables which would be customer satisfaction and overall delivery time versus expected estimate, though it will also likely result in some sort of bias that has to be accounted for.”
More Example Questions
A large part of the interview will focus on your leadership style.
People management questions: In particular, these questions will assess your style of communication and team management. These questions will focus on how you identify talented hires, how you motivate your team, how you resolve conflicts, and how you provide coaching to each team member.
Product leadership questions: Additionally, leadership questions in manager interviews focus on your ability to maximize the impact data science can have across the entire organization. These types of questions focus on your collaboration skills, assess your ability to gain buy-in for data science, and determine if you’re a good fit with the executive leadership team.
Here are some quick tips to nail leadership questions:
Question: How do you describe your leadership style?
Answer: I consider myself a goal-oriented and transformational leader. I encourage my team to align their work to the company’s overarching goals. Previously, I held quarterly meetings to review our work and track the progress of overall goals. During one of those meetings, we discovered that one of our projects wasn’t properly aligned to company goals, as it was too department-focused, so we updated that goal to better align with the company’s North Star goals.
More Example Questions
As you practice data science manager questions, remember these key interviewing tips:
Facebook’s data science manager interview process includes a recruiter call, a personal call, a technical interview, and an on-site interview. Overall, the questions will likely fall into the categories of leadership, behavioral, or product leadership.
In general, the technical screen for Facebook data science manager roles usually focuses on SQL and product case studies.
See our guide Facebook Data Science Interview Questions for more practice questions.
We’re given two tables.
friend_requests holds all the friend requests made and
friend_accepts is all of the acceptances. Write a query to find the overall acceptance rate of friend requests.
See a full solution to this question.
Write a query to get the distribution of total push notifications before a user converts.
More Context: In this question, we’re given two tables, a table of
notification_deliveries and a table of
created and purchase conversion dates. If the user hasn’t purchased then the
conversion_date column is
created_at) Find the overall acceptance rate of requests.
action(sent, accepted, rejected)
1.Why is the average number of comments per user decreasing and what metrics would you look into?
More context: Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.
The company has been consistently growing new users in the city from January to March. In this question, start by calculating the average comments per user, which you can do by modeling the data.
Hint: You’re given information that total user count is increasing linearly, which means the decreasing comments/user is not an effect of a declining user base.
2.Determine whether adding a feature identical to Instagram Stories to Facebook is a good idea.
This hard Meta business sense question is similar to what you can expect in data manager interviews.
3.Let’s say that you’re a data scientist on the engagement team. A product manager comes up to you and says that the weekly active users metric is up 5%, but email notification open rates are down 2%.
What would you investigate to diagnose what’s happening?
Hint: What is the expected relationship between weekly active users and email open rates? How does this affect our analysis of why we see one increase while the other decreases? See a full solution for this question .