Notion Labs Data Scientist Interview Questions + Guide in 2025

Notion Labs is on a mission to empower individuals and teams to tailor their software to solve any challenge, transforming how people interact with technology to enhance productivity.

As a Data Scientist at Notion Labs, you will play a pivotal role in leveraging data to drive the growth and success of the company. Your responsibilities will include conducting in-depth analyses to uncover insights that inform product and business decisions, collaborating closely with product, engineering, and growth teams to identify opportunities for improvement, and designing experiments to evaluate the effectiveness of new features and product changes. The ideal candidate will have strong expertise in statistical analysis, experience working with large datasets, and the ability to communicate findings effectively to cross-functional teams. Familiarity with SQL and at least one scripting language, such as Python or R, is essential. A successful Data Scientist at Notion should also be comfortable navigating ambiguity, possess a growth mindset, and demonstrate a bias for action that aligns with the company's commitment to continuous learning and improvement.

This guide will help you prepare for your interview by providing insights into the expectations for the role, the skills you should emphasize, and the types of questions you may encounter, enabling you to stand out as a candidate who aligns with Notion's values and mission.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Notion Labs. The interview process will likely assess your technical skills, problem-solving abilities, and cultural fit within the company. Be prepared to discuss your past experiences, demonstrate your analytical skills, and showcase your understanding of data science principles.

Technical Skills

1. Can you describe a data project you worked on that had a significant impact on your team or organization?

This question aims to understand your practical experience and the value you can bring to Notion.

How to Answer

Focus on a specific project, detailing your role, the challenges faced, and the outcomes achieved. Highlight how your work influenced decision-making or improved processes.

Example

“I led a project analyzing user engagement metrics for our product. By implementing a predictive model, we identified key features that drove user retention, resulting in a 15% increase in active users over three months. This analysis not only informed our product roadmap but also helped the marketing team tailor their campaigns effectively.”

2. How do you approach data cleaning and preparation?

Data preparation is crucial for accurate analysis, and this question assesses your methodology.

How to Answer

Discuss your systematic approach to data cleaning, including tools and techniques you use to ensure data quality.

Example

“I typically start by assessing the dataset for missing values and outliers. I use Python libraries like Pandas for data manipulation, applying techniques such as imputation for missing values and normalization for outliers. This ensures that the data is reliable and ready for analysis.”

3. Explain a time when you used statistical inference to make a decision.

This question evaluates your understanding of statistical methods and their application in real-world scenarios.

How to Answer

Provide a specific example where statistical inference played a key role in your decision-making process.

Example

“In a previous role, I conducted A/B testing to evaluate two different user interface designs. By analyzing the conversion rates using statistical significance tests, I was able to recommend the design that improved user engagement by 20%, which was implemented across the platform.”

4. What experience do you have with predictive modeling?

Predictive modeling is a key skill for a data scientist, and this question assesses your expertise in this area.

How to Answer

Discuss the types of models you’ve built, the data used, and the results achieved.

Example

“I have built several predictive models using regression analysis and machine learning algorithms. For instance, I developed a model to forecast sales based on historical data, which improved our forecasting accuracy by 30%. I utilized Python’s Scikit-learn library for model training and evaluation.”

5. How do you ensure the accuracy and reliability of your data analyses?

This question assesses your attention to detail and commitment to data integrity.

How to Answer

Explain the steps you take to validate your analyses and ensure the results are trustworthy.

Example

“I implement a multi-step validation process, including cross-validation techniques and peer reviews of my analyses. Additionally, I document my methodologies and findings thoroughly, allowing for reproducibility and transparency in my work.”

Behavioral Questions

1. Describe a time you faced ambiguity in a project. How did you handle it?

This question evaluates your problem-solving skills and adaptability in uncertain situations.

How to Answer

Share a specific instance where you navigated ambiguity, focusing on your thought process and actions taken.

Example

“During a project to analyze user feedback, the initial data was incomplete and lacked context. I organized brainstorming sessions with stakeholders to clarify objectives and gather additional qualitative data. This collaborative approach helped us redefine our analysis and ultimately led to actionable insights.”

2. How do you prioritize your tasks when working on multiple projects?

This question assesses your time management and organizational skills.

How to Answer

Discuss your prioritization strategy and how you balance competing demands.

Example

“I use a combination of project management tools and frameworks like the Eisenhower Matrix to prioritize tasks based on urgency and importance. This helps me focus on high-impact projects while ensuring that deadlines are met across all initiatives.”

3. Can you give an example of how you communicated complex data findings to a non-technical audience?

This question evaluates your communication skills and ability to convey technical information effectively.

How to Answer

Provide an example where you simplified complex data insights for a non-technical audience.

Example

“I once presented the results of a user behavior analysis to the marketing team. I created visualizations using Tableau to illustrate key trends and insights, avoiding technical jargon. This approach facilitated a productive discussion on how to leverage the findings for our upcoming campaign.”

4. What motivates you to work in data science?

This question helps interviewers understand your passion and commitment to the field.

How to Answer

Share your motivations and what excites you about data science.

Example

“I am motivated by the power of data to drive meaningful change. The ability to uncover insights that can influence product development and enhance user experiences is incredibly fulfilling. I enjoy the challenge of solving complex problems and continuously learning in this dynamic field.”

5. Why do you want to work at Notion?

This question assesses your interest in the company and alignment with its values.

How to Answer

Discuss what specifically attracts you to Notion and how your values align with the company’s mission.

Example

“I admire Notion’s commitment to empowering users through customizable tools. The focus on collaboration and innovation resonates with my own values. I am excited about the opportunity to contribute to a product that enhances productivity and creativity for individuals and teams alike.”

Here are some tips to help you excel in your interview.

Understand the Interview Structure

Familiarize yourself with the typical interview process at Notion, which often includes an initial recruiter call, a technical screening, and multiple rounds of interviews that may cover coding, data modeling, and behavioral questions. Knowing the structure will help you prepare effectively and manage your time during the interview.

Prepare for Technical Challenges

Expect to face practical coding challenges that may involve SQL, Python, or R. Review common data manipulation tasks and be ready to demonstrate your ability to build predictive models and evaluate their effectiveness. Practice coding in a live environment, as some interviews may include real-time coding assessments.

Emphasize Collaboration and Communication

Notion values strong communication and collaboration skills, especially in cross-functional environments. Be prepared to discuss how you have worked with product and engineering teams in the past, and provide examples of how your data-driven insights have influenced product decisions. Highlight your ability to communicate complex data findings in a clear and actionable manner.

Showcase Your Problem-Solving Skills

During the interview, you may encounter ambiguous problems that require you to think critically and creatively. Be ready to discuss how you approach problem-solving, including your methods for defining problems, identifying opportunities, and designing experiments to evaluate potential solutions.

Align with Company Values

Notion places a strong emphasis on its mission to empower users through toolmaking. Familiarize yourself with the company's values and culture, and be prepared to articulate how your personal values align with theirs. Demonstrating a genuine interest in Notion's mission can set you apart from other candidates.

Be Ready for Behavioral Questions

Expect behavioral questions that explore your past experiences and how you handle various situations. Prepare examples that showcase your adaptability, teamwork, and ability to navigate challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Stay Engaged and Ask Questions

Throughout the interview process, maintain an engaging demeanor and show enthusiasm for the role and the company. Prepare thoughtful questions to ask your interviewers about the team dynamics, company culture, and future projects. This not only demonstrates your interest but also helps you assess if Notion is the right fit for you.

Follow Up Thoughtfully

After your interviews, send a personalized thank-you note to your interviewers, expressing your appreciation for their time and reiterating your interest in the role. This small gesture can leave a positive impression and reinforce your enthusiasm for joining the team.

By following these tips and preparing thoroughly, you'll be well-equipped to make a strong impression during your interview at Notion. Good luck!

The interview process for a Data Scientist role at Notion Labs is structured and thorough, designed to assess both technical skills and cultural fit. Candidates can expect a series of interviews that evaluate their analytical capabilities, problem-solving skills, and ability to collaborate across teams.

1. Initial Recruiter Call

The process typically begins with a 30-minute introductory call with a recruiter. This conversation serves to gauge your interest in the role and the company, as well as to discuss your background and experiences. The recruiter will also provide insights into Notion's culture and the expectations for the Data Scientist position.

2. Technical Screening

Following the initial call, candidates will undergo a technical screening, which may include a live coding challenge or a take-home project. This stage focuses on your proficiency in relevant programming languages (such as Python or R) and your ability to manipulate data effectively. Expect to solve practical problems that reflect real-world scenarios you might encounter in the role, such as building predictive models or conducting data analyses.

3. Onsite Interviews

The onsite interview consists of multiple rounds, typically including both technical and behavioral assessments. Candidates can expect to participate in coding interviews, data modeling exercises, and discussions about past projects. Interviewers will assess your ability to communicate complex ideas clearly and your approach to problem-solving. Additionally, there may be a round focused on cross-functional collaboration, where you will discuss how you work with product and engineering teams.

4. Leadership Interview

As part of the final stages, candidates will have an interview with a member of the leadership team, which may include the Head of Data or other senior executives. This round is designed to evaluate your alignment with Notion's values and your potential impact on the company's growth. Expect questions that explore your vision for data science and how you can contribute to Notion's mission.

5. Reference Check

After successfully completing the interview rounds, candidates may undergo a reference check. This step is crucial for verifying your past experiences and ensuring that you are a good fit for the team and company culture.

The interview process at Notion Labs is known for its thoughtful and engaging approach, allowing candidates to showcase their skills while also assessing the company's fit for their career aspirations.

Now, let's delve into the specific interview questions that candidates have encountered during this process.

Here are a few questions that are frequently asked in Notion Labs Data Scientist interviews:

1. What would your current manager say about you? What constructive criticisms might he give?
2. What are you looking for in your next job at Notion?
3. How do you resolve conflicts with your co-workers and external stakeholders?
4. How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
5. Tell me about a project in which you had to clean and organize a large dataset.
6. What factors could have biased Jetco’s study showing the fastest average boarding times, and what would you investigate?
7. You are testing hundreds of hypotheses with many t-tests. What considerations should be made?
8. How do you write a query to randomly sample a row from a table with over 100 million rows without throttling the database?
9. Why might the average number of comments per user decrease despite growing new users, and what metrics would you investigate?
10. In an A/B test, how can you check if assignment to the various buckets was truly random?
11. Calculate the first touch attribution channel for each user_id that converted based on their initial session when they first discovered the website.
12. Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value. How would you assess the validity of the result?
13. What’s the difference between Lasso and Ridge Regression?
14. Write a query to calculate the three-day rolling average of daily deposits from a bank transactions table.
15. What is the probability that a user receives exactly 0 impressions, and the probability that every user receives at least 1 impression, given an audience of size A and B random impressions?
16. How would you set up an A/B test to evaluate changes to a button’s color and position in a sign-up funnel?
17. Given the probability of rain tomorrow based on whether it rained today and yesterday, write a function to calculate the probability of rain on the nth day after today.
18. How would you build a fraud detection model with a text messaging service for customer approval or denial?
19. Given limited resources, how would you determine which new feature to prioritize for development based on user data and business goals?
20. How would you approach the task of automatically summarizing long documents within Notion using natural language processing techniques?

At Interview Query, we strive to hear from successful data scientists about their interview experiences and transitions from various fields. To help you prepare and make the leap into data science, today we’re sharing the story of Hoda Noorian, a startup founder, venture capitalist, and chemical engineer who has made it large as a Data Scientist at Notion and now leads her own team.

How did you get into data science?

In her interview, Hoda tells us how she began studying chemical engineering in Iran but shifted to entrepreneurship by co-founding a startup, Barx. This led to her securing a scholarship to study entrepreneurship at UC Berkeley. In the US, she pivoted to data science, taking relevant courses and working as a venture capital analyst.

She then earned a scholarship for the data science program at the University of San Francisco, interned at Airbnb on machine learning projects, and joined Carbon Health as an early data scientist. After three and a half years there, she recently joined Notion as a data scientist to face new challenges.

“I began my undergraduate studies in chemical engineering at one of the top universities in Iran. However, I quickly realized I wasn’t interested in the coursework or the industry’s future.

Struggling to find relevance in my studies, I was introduced to entrepreneurship at age 19 or 20. I co-founded a startup called Barx, which aimed to be an “Uber for the internet.”

This venture exposed me to the startup ecosystem, and we even attended international conferences. Winning a conference in Berlin called IBridges earned me a scholarship to study entrepreneurship at UC Berkeley during a summer course.

This opportunity brought me to the United States.

Upon arriving in the US, I initially planned to pursue an MBA but soon decided I wanted to stay technical. I pivoted my focus and started taking prerequisite courses in computer science, linear algebra, and advanced statistics to prepare for a data science master’s program.

During this time, I worked as a venture capital analyst for almost two years, gaining valuable experience evaluating products and technologies.

I was accepted into the University of San Francisco’s data science program with a substantial scholarship.

While there, I interned at Airbnb, where I worked on the ethical implications of experimentation. My role was machine learning-heavy, involving projects like building a model to infer gender from various socio-economic factors.

This experience also included educating other data scientists on the ethical use of such models and conducting studies to identify potential biases in past experiments.

Graduating during the onset of COVID-19, I found the job market challenging, but it felt like the right time to join a healthcare company.

I joined Carbon Health as one of their early data scientists and stayed for three and a half years. At Carbon Health, I led growth experimentation efforts, focusing on A/B testing and understanding user friction.

This role involved high levels of ownership and responsibility, working closely with executives to align data science initiatives with company goals. I briefly served as a data science manager but returned to an individual contributor role after organizational restructuring.

A month ago, I joined Notion, excited to embrace new challenges and opportunities in a different sector.”

Could you elaborate on the challenges you faced as a data scientist manager at Carbon Health?

As a data science manager at Carbon Health, she faced challenges during the company’s restructuring and layoffs, which resulted in managing a smaller team with limited resources. She had to balance leading complex growth experiments and A/B testing with aligning efforts to the company’s top priorities, requiring both technical expertise and effective communication with executives.

“When I became a data scientist manager at Carbon Health, one of the main challenges I faced was navigating the company’s restructuring during a period of layoffs.

This created an environment of uncertainty and required me to manage a smaller team with limited resources. Another challenge was ensuring the high ownership and responsibility expected from our data science team.

I had to balance leading the growth experimentation efforts, which involved complex A/B testing and user friction analysis, while also aligning with the company’s top priorities. The role required a deep understanding of both technical and business aspects to effectively communicate and collaborate with executives.”

What were you looking for in a new role and why did you decide to move to Notion?

She decided to move to Notion for several reasons. After over three years at Carbon Health, she felt ready for new challenges and sought to diversify her experience beyond healthcare data science. Notion’s potential for building an AI-connected workspace, its integration of user knowledge and advanced tools, and the impressive quality of its team were major attractions. The innovative product and growth trajectory of Notion also contributed to her decision.

“I decided to move to Notion for a few reasons. First, after spending over three years at Carbon Health, I felt that I had gained valuable experience and learned a lot, but I was ready for new challenges.

At Carbon, my role was heavily focused on conversion optimization and user experience within the healthcare domain, which eventually started to feel limiting. I didn’t want to be pigeonholed as just a “healthcare data scientist,” so I looked for opportunities that would allow me to diversify my experience.

I was particularly drawn to Notion because of the company’s potential to build an AI-connected workspace. Notion stood out because they have what it takes to integrate user knowledge, good documentation, and advanced tools to create a seamless and automated user experience. The quality of people at Notion was also a significant factor.

The team was not just talented but consistently impressive across the board, which was exciting to me. I wanted to work with and learn from such a high-caliber team. Additionally, the innovative nature of Notion’s product and its growth trajectory were very appealing.”

Can you discuss your experiences and preparation for the data science interviews, particularly at Notion?

Her experience with the interview process at Notion was very positive. The process was fast and well-organized, taking about one and a half months in total. Preparation involved brushing up on SQL, Python, pandas, and revisiting A/B testing and statistics concepts.

Structured guidance from Interview Query and Emma’s videos on product data science questions were particularly helpful. While the technical phone screening initially stressed her, the questions were straightforward. The process also included two rigorous behavioral interviews focused on cultural fit, which she found valuable for ensuring alignment with Notion’s values. Overall, thorough preparation on practical skills and fundamentals contributed to her confidence and success.

“My experience with the interview process at Notion was very positive. The process was fast and well-organized, which made it stand out from other companies I interviewed with, like DoorDash and PayPal. The recruiter at Notion had reached out to me a couple of years before, but the timing wasn’t right back then.

When I was ready to move, I reconnected with them, and the interview process took about one and a half months in total.

To prepare for the interviews, I focused on brushing up my SQL and Python skills, particularly using pandas for data manipulation. I practiced solving product questions and revisited my notes and coursework on A/B testing and statistics.

*Interview Query, in particular, provided structured guidance and practice questions that helped me solidify my understanding and approach product data science questions effectively. I also relied heavily on Emma’s videos on product data science questions, which provided a great structure for approaching these types of problems.*

My preparation was less about mastering the most complex topics and more about ensuring I had a solid understanding of the fundamentals and could apply them effectively.

One challenge during the interview was the technical phone screening. I was asked to have my notebook ready to share my screen, which initially stressed me out as I wasn’t sure what to expect.

However, the questions were straightforward, involving SQL and Python tasks that I was comfortable with.

Notion’s interview process also included two rounds of behavioral interviews. They were rigorous and focused on cultural fit, which I appreciated because it showed how much they value their work environment.

Some of the behavioral questions were tough, such as describing the worst manager I had or the cultural factors I disliked in previous jobs. These questions required deep reflection but ultimately helped ensure alignment with Notion’s values.

Overall, my preparation was thorough and targeted, focusing on practical skills and fundamental knowledge, which helped me feel confident and perform well during the interviews.”

Are there any significant lessons or experiences that have profoundly shaped your professional philosophy?

Two significant lessons have shaped her professional philosophy:

• Education vs. Work: She learned that work differs from education in that there isn’t always a correct answer and managers might not have all the solutions. This realization taught her to be independent, trust her judgment, and proactively find solutions.
• Aligning Success: True success comes from aligning personal goals with team objectives and the company’s mission. This alignment creates a significant impact and provides personal satisfaction and purpose.

These lessons have guided her career transitions from chemical engineering to startups, venture capital, and data science.

“There are two significant lessons that have profoundly shaped my professional philosophy:

Understanding the Difference Between Education and Work

When I started my career, it took me some time to realize that work is very different from education. In school, there is usually a correct answer, and someone has it.

At work, no one has the correct answer, and your manager isn’t necessarily testing you—they often don’t know the answer either.

Understanding this distinction was crucial for me. It taught me to be more independent, to trust my judgment, and to be proactive in finding solutions.

Finding the Overlap Between Personal, Team, and Company Success

Over the years, I’ve learned that true success comes from understanding where your personal goals, your team’s objectives, and the company’s mission overlap.

When you can align your work with what’s important to your team and the company, you create a significant impact. This alignment not only drives results but also brings personal satisfaction and a sense of purpose to your work.

These lessons guided me from chemical engineering to startups, venture capital, and data science. They kept me focused and motivated, and I hope they inspire others in their careers.”

What advice would you give to others looking to enter the tech field or transition within it?

She says to those entering or transitioning within the tech field to stay flexible and continuously seek learning opportunities. Her journey began with a degree in chemical engineering but shifted after co-founding a startup and winning a scholarship to study entrepreneurship at UC Berkeley. This experience led to a strong product mindset and a transition to data science through foundational courses and work as a venture capital analyst. Embrace learning, adapt to new challenges, and use available resources to enhance skills and knowledge. This approach has been critical to her success.

“For those looking to enter the tech field or transition within it, my advice is to stay flexible and continuously seek new learning opportunities.

My journey began with a degree in chemical engineering, which I found uninspiring and unaligned with my interests. The turning point came when I co-founded a startup called Barx and won a scholarship to study entrepreneurship at UC Berkeley.

This experience exposed me to the dynamic world of startups and venture capital, where I developed a strong product mindset. Transitioning to data science, I took foundational courses in computer science, linear algebra, and statistics while working as a venture capital analyst.

Always be open to learning and adapting, leveraging available tools and resources to enhance your skills and knowledge. This combination of technical education, practical experience, and a willingness to pivot when necessary is the key factor that has led me to where I am today.”

As discussed by Hoda, preparing for a Data Scientist interview at Notion Labs involves a blend of technical skill sharpening, understanding the company’s needs, and showcasing your problem-solving and communication abilities. Here’s a guide to help you prepare more efficiently:

Understand the Role and Notion’s Values

Firstly, understand the specific requirements and responsibilities of the Data Scientist position you’re applying for. This includes key metrics they focus on and any recent projects or innovations. Furthermore, familiarize yourself with Notion’s products, particularly their AI-connected workspace, and how they integrate user knowledge and tools to prepare for specific technical components of the platform.

Brush Up on Technical Skills

Coding is an integral part of data science. Practice coding in Python and SQL, focusing on data manipulation, cleaning, and analysis. Review common ML algorithms, their applications, and how to implement them. Prepare to discuss and solve machine learning problems, and do whiteboard coding.

Moreover, ensure you have a solid grasp of fundamental concepts of statistics, A/B testing, and experimental design. Also, be prepared to analyze datasets and draw insights from large datasets.

Prepare for the Behavioral Interviews

As mentioned, behavioral interviews carry significant weight in Notion Labs’ data scientist interview process. Prepare to discuss your past experiences, challenges, and how you’ve worked with teams.

Reflect on questions related to cultural fit, leadership, and decision-making. We also recommend practicing product sense questions to refine your critical-thinking skills.

Practice With Mock Interviews

Mock interviews are great for simulating interview experiences. Practice through our P2P Mock Interview Portal and AI Interviewer to refine our responses to technical and behavioral Notion Labs data scientist interview questions.

What is the average salary for a Data Scientist role at Notion Labs?

We don’t have enough data points to render this information. Submit your salary and get access to thousands of salaries and interviews.

What other companies are hiring Data Scientists besides Notion Labs?

In addition to Notion Labs, companies like Meta, Airbnb, and LinkedIn are actively hiring Data Scientists, offering opportunities across various industries and specialties.

Does Interview Query have job postings for the Notion Labs Data Scientist role?

Yes, we feature job postings for the Notion Labs data scientist roles among other companies and roles on our Job Board. We also recommend going through the specific company career pages for faster updates.

The Notion Labs data scientist interview process is stringent, focusing on problem-solving, technical skills, and cultural fit. Be prepared to discuss your experience with data analysis, machine learning, and product development, as well as your ability to collaborate effectively and adapt to a fast-paced environment.

In addition to data scientist roles, Notion Labs also offers positions in Product Management, Business Intelligence, and Data Analyst roles. If you’re interested in joining a dynamic and innovative company, consider exploring our Notion Labs interview guide. All the best!