Interview Guide Nov 28
Nov 283 rounds
Detailed, specific guidance on the Pinterest Data Scientist interview process - with a breakdown of different stages and interview questions asked at each stage
A Data Scientist at Pinterest is responsible for collecting, analyzing, and interpreting large amounts of data to gain insights and inform business decisions. The data collected and analyzed can range from user behavior and engagement data to financial and marketing data.
A Data Scientist at Pinterest works closely with other teams such as product, engineering, and marketing to understand their needs and provide data-driven solutions to complex problems. They also work on developing predictive models and algorithms to help the company make informed decisions about product development, marketing campaigns, and resource allocation.
Pinterest hires Data Scientists across the company and there are different seniority levels depending on the scope and expected impact. They have Senior, Principal, Director and Manager level roles. They also have openings for ML engineers and Applied Research Scientists. For instance, there was recently a role in Ads Quality team as a ML Engineer - the goal being to develop and execute a vision for the evolution of the machine learning technology stack within Ads Ranking. You will work on tackling new challenges in machine learning and deep learning to advance the statistical models that power the ads engagement and delivery that bring together Pinners (i.e. what Pinterest calls its employees) and partners in this unique marketplace.
It is worth noting that the available positions and locations can change frequently, so it is recommended to regularly check the career page for the latest updates.
How to Apply for a Data Scientist Job at Pinterest?
To apply for a Data Scientist job at Pinterest, you will need to visit the Pinterest's career website and search for open Data Scientist positions. Once you have found a position that you are interested in, you will be able to submit an application online. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances meaningfully. One tip regarding your resume - make a few tweaks for the position and the role you are applying for which will help you have a better chance compared to other candidates. For instance, some DS roles might explicitly call for a background in ML; others might need you to be very good at visualization - these are exactly the sort of things you should then highlight if you have past experience doing. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.
As a part of the Pinterest Data Scientist interview, the candidate will need to go through multiple interview rounds:
1. Phone Screening - The phone screening will be a conversation with a recruiter, detailing your technical background, your past relevant projects, and a quick assessment of your skill sets based on your resume.
2. Technical Interview - The second primary round of an interview process at Pinterest involves a technical interview with a senior data scientist. This interview is designed to assess your technical skills and knowledge in the field of data science.
3. Onsite Interview Rounds - The final onsite primary round of an interview process at Pinterest is an extensive evaluation of your technical and professional skills. This onsite interview consists of multiple rounds designed to assess different aspects of your expertise and capabilities.
Get a mock interview with a recruiter at Pinterest→ Schedule Now
Check out video guide that delves into the interview process and provides valuable tips tailored to each round of the interview.
The first primary round of an interview process at Pinterest typically begins with an initial phone conversation with a recruiter. During this call, the recruiter will delve into your technical background, past relevant projects, and a quick assessment of your skills based on your resume. They will also provide an overview of Pinterest's company culture and values, and how they align with your career goals and aspirations.
- Why do you want to join Pinterest?
- Why do you think you will be a good fit for the role?
- What responsibilities do you expect to have from your job at Pinterest?
The second primary round of an interview process at Pinterest involves a technical interview with a senior data scientist. This interview is designed to assess your technical skills and knowledge in the field of data science.
During this interview, you can expect to be asked a case study question about metrics analysis, python coding questions, and an experimentation question. These questions are designed to test your understanding of the concepts and techniques used in data science, as well as your ability to apply them in real-world situations.
In addition to the technical questions, the interviewer will also engage you in a discussion around a past project, the approaches you used, and how you solved certain challenges. Some candidates also reported some light SQL coding involved in this interview, to test your proficiency in working with databases and retrieving data.
Pinterest uses “Karat” for almost all their technical interviews and the Data Scientist technical screening is also done using the shared screen Karat platform.
- Walk us through a recent project where you were tasked with analyzing and improving a set of metrics for a particular product or business unit?
- How did you approach analyzing the data to identify trends and patterns?
- Write a function to calculate the mean, median, and mode of a given list of numbers in Python?
- Explain how to handle missing data when working with a large dataset in Python?
- Give an example of a past experiment you designed and executed to test a hypothesis about a particular product or feature?
- How did you go about determining the appropriate sample size and measurement metrics for your experiment?
- Can you tell us about a particularly challenging data science project you worked on in the past? How did you approach solving the problem and what methods did you use?
- Write a SQL query to retrieve the top 10 most frequently purchased items from a sales database?
- Write a SQL query to calculate the average purchase price for each customer in a sales database?
Read these articles
The final onsite primary round of an interview process at Pinterest is an extensive evaluation of your technical and professional skills. This onsite interview consists of multiple rounds designed to assess different aspects of your expertise and capabilities. Some of the rounds you can expect to face are:
- ML Interview: This interview is a machine learning assessment that has two parts. The first part involves two coding questions, where you will be required to demonstrate your programming skills. The second part is a set of multiple-choice questions about deep learning and machine learning, which will test your theoretical knowledge in the field.
- Statistics and Probability Interview: In this round, you will be assessed on your understanding of statistics and probability concepts, and how you can apply them in data science.
- Behavioral Interview: This round focuses on your professional conduct, communication skills, and team dynamics. The interviewer will ask questions to get a better understanding of your work style, how you handle challenges, and your approach to problem-solving.
- Online Assessment: This round of the onsite interview is an online assessment that includes two topics - Hackerank DFS (Depth-First Search) and BFS (Breadth-First Search). This assessment will test your coding skills and ability to implement algorithms, which are critical skills in the field of data science.
Please note that this round is role specific and you might encounter a different case depending on the role you are applying for.
- Explain the difference between supervised and unsupervised learning?
- Give an example of a time when you used a decision tree for a project?
- Walk us through how you would build a recommendation system
- Describe how a neural network works.
- Can you explain Bayes' Theorem and how it is used in data science?
- Give an example of a hypothesis test you have conducted in the past?
- How would you determine if a given data set is normally distributed?
- Explain the difference between a confidence interval and a prediction interval?
- Walk us through a time when you had to handle a difficult situation with a team member or stakeholder?
- How do you approach problem-solving when faced with a complex data science project?
- Can you tell us about a time when you had to communicate technical information to a non-technical audience?
- Explain the difference between DFS and BFS? Can you give an example of a problem that would be better solved using DFS?
When you are preparing for a Pinterest Data Science interview - we’d recommend keeping the following in mind:
- Research about Pinterest's company culture, values, and goals to align them with your career aspirations. You can check out Pinterest Life to know more about the company culture.
- Brush up on your technical skills and knowledge in data science, including metrics analysis, python coding, and experimentation.
- Prepare to answer questions about a past project, including your approach, solutions to challenges and SQL coding.
- Brush up on your machine learning, statistics, and probability concepts, and be able to apply them in data science.
- Be ready for the behavioral interview, which will focus on your professional conduct, communication skills, and team dynamics.
- Prepare for the online assessment, which will test your coding skills and ability to implement algorithms.
- Be ready to answer questions about handling difficult situations with team members or stakeholders and your approach to problem-solving in complex data science projects.
Responsibilities of a Data Scientist at Pinterest
The responsibilities of a data scientist at Pinterest across roles can broadly be seen as-
- Design features and build large-scale machine learning models to improve user ads engagement prediction. For instance, you will develop a recommendation algorithm that predicts which ads are most likely to be engaged with by users based on their past behavior and interests.
- Develop new techniques for inferring user interests from online activity. For instance, you will design a machine learning model that predicts user interests based on their search history and the topics they pin on Pinterest.
- Mine text, visual, user signals to better understand user intention. Use natural language processing techniques to analyze user-generated text data, such as comments and boards, to understand their interests and preferences.
- Design and implement core metrics that serve as the north stars for team efforts and help design and evaluate A/B experiments that drive these metrics.
- Help gather explicit signals about Pinner, Merchant, or Advertiser preferences and tastes using survey methodologies and statistical/modeling based approaches.
- Work cross-functionally to build and communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest
Skills and Qualifications needed for Data Scientists at Pinterest
Some of the skills and qualifications that may be required for a Data Scientist at Pinterest include:
- It's beneficial to have at least 5+ years of experience in Data Science roles, which can help you stand out from other candidates.
- Acquire a strong foundation in mathematics and stay up-to-date with statistical methods to excel in a Data Scientist role at Pinterest.
- Focus on building production-level machine learning systems that can handle large-scale data with efficiency. This would require experience in data mining, search, recommendations, and natural language processing.
- Keep yourself informed about best practices in data manipulation, building data pipelines, feature engineering, and dashboard creation. These skills are essential for extracting meaningful insights from data.
- Develop your skills in working with high-dimensional and complex data sets. Familiarity with SQL and fluency in either Python or R will come in handy.
- Be proactive and take the lead in cross-functional initiatives. This would help you drive meaningful results and effectively communicate findings with leadership and product teams. This will ultimately lead to quick and effective action on data-driven insights.
The salary range for a Data Scientist at Pinterest would depend on several factors such as the person's experience, location, and the specific role they are hired for. However, the average salary for a Data Scientist at Pinterest is approximately $135,000 to $165,000 per year, with top earners making over $180,000 per year. Keep in mind that these figures are just rough estimates and the actual salary could be higher or lower based on the specific factors mentioned earlier.
The interview process for a Data Scientist role at Pinterest typically includes 3 primary rounds - a phone screening, a technical round, and the final onsite interview rounds. The phone screening will be a conversation with a recruiter, detailing your technical background, your past relevant projects, and a quick assessment of your skill sets based on your resume. The second primary round of an interview process at Pinterest involves a technical interview with a senior data scientist. This interview is designed to assess your technical skills and knowledge in the field of data science. The final onsite primary round of an interview process at Pinterest is an extensive evaluation of your technical and professional skills. This onsite interview consists of multiple rounds designed to assess different aspects of your expertise and capabilities.
Schedule a free peer interview to practice for the interview.→ Schedule Now
What qualifications and skills are typically required for a Data Engineer role at Pinterest?
Qualifications often include a bachelor's or master's degree, prior experience in data engineering or related fields, proficiency in programming languages (e.g., Python, SQL), and expertise in data processing frameworks and technologies.
What data engineering topics and technologies are commonly covered in Pinterest's Data Engineer interviews?
Expect questions related to data modeling, ETL (Extract, Transform, Load) processes, data pipelines, distributed computing, and your ability to design scalable and efficient data systems.
Is there a coding assessment or programming component in the Data Engineer interview process?
Yes, coding interviews are common, where you may be asked to solve data engineering problems, write algorithms, and demonstrate your coding skills in languages like Python or SQL.
How can I prepare for the technical interviews in the Data Engineer interview process at Pinterest?
Review data engineering principles, practice coding problems related to data processing, and be ready to discuss your past experiences in designing and optimizing data systems.