Notion Labs Data Scientist Interview Questions + Guide 2024

Launched in 2016, Notion Labs grew from 1 million to 30 million users last year. Notion Lab’s flagship product, the Notion platform, offers a versatile all-in-one workspace that combines note-taking, task management, wiki, and database features. What started as a simple note-taking app now helps individuals and teams organize their thoughts, projects, and information flexibly and customizable.

Notion Labs primarily focuses on enhancing organizational and individual productivity. Data scientists there play a pivotal role in ensuring user satisfaction with the platform’s performance, features, and scalability. As a data scientist, you’ll be responsible for identifying user pain points, predicting needs, assessing churn risk, and prioritizing and personalizing features.

Your interest in joining this growing platform has led you to the right place. In this article, we’ll cover the interview process, explore common interview questions, and share the success story of Hoda Noorian from Notion Labs.

You’ll get more insight into the interview process for data scientist roles at Notion Labs from our interview with Hoda Noorian, but it typically involves several stages designed to assess both technical skills and cultural fit. Here’s an overview of what you might expect:

Initial Screening Call

After your resume has been shortlisted, you’ll be asked to hop in a 30-minute conversation with a recruiter. They’ll ask about your background, experience, and why you’re interested in working at Notion. They’ll also give you an overview of the role and the company culture. They might also delve into a few predefined technical questions to assess your skill levels and if they align with the requirements of the role.

Technical Assessment Rounds

You’ll likely face one or more coding interviews focused on algorithms, data structures, and problem-solving skills during the technical assessment rounds. These are typically conducted on virtual platforms or shared code editors. As a data scientist candidate, expect to solve problems involving data analysis, possibly using SQL, Python, or R. You may be asked to analyze a dataset, perform A/B testing, or work through statistical problems.

If your role of interest has a strong ML component, you might be asked to discuss your experience with ML models, including how you’ve built and deployed them in the past.

Behavioral Interview Rounds

Typically, two behavioral rounds are conducted for the data scientist interviews at Notion Labs. These interviews focus on cultural fit and how well you align with Notion’s values. You’ll be asked about your past experiences, how you handle challenges, work in teams, and your motivations. Questions might also explore how you approach learning and growth.

On-site or Virtual Team Interviews

The final stage for Notion data scientist interviews typically involves multiple interviews with team members, including potential colleagues, engineering managers, and other data scientists. These interviews might delve deeper into the technical aspects, along with discussions about your previous work and how you might contribute to the team at Notion.

Depending on your role and position, a conversation with a co-founder or senior leadership to ensure alignment with the company’s mission and vision may also be conducted.

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?

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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!