Snap Inc. is a leading technology company focused on enhancing communication and self-expression through innovative products like Snapchat, Lens Studio, and Spectacles.
As a Data Scientist at Snap Inc., you will play a crucial role in leveraging data to inform product decisions and enhance user experiences. Your responsibilities will include applying your expertise in quantitative analysis, data mining, and statistical modeling to derive actionable insights that can drive product improvements. You will collaborate closely with product managers, engineers, marketers, and designers in a dynamic and creative environment that values operational excellence and cross-functional collaboration.
Key responsibilities of this role include the design, implementation, and tracking of core metrics to analyze product performance, conducting rigorous machine learning and statistical analyses, and effectively communicating findings through visuals, dashboards, and reports. A successful Data Scientist at Snap will possess strong data modeling skills to identify trends and opportunities, demonstrate an understanding of Snapchat's ecosystem, and communicate complex analyses in a clear and actionable manner.
To excel in this position, candidates should have a BS/BA degree in a technical field, at least 5 years of experience in quantitative analysis and data science, and proficiency in SQL and programming languages such as Python or R. Experience in statistical techniques such as A/B testing and a solid understanding of product-focused roles in social media or mobile technology are highly valued.
This guide aims to equip you with the knowledge and strategies necessary to prepare effectively for your interview, helping you to showcase your expertise and align your skills with the innovative vision of Snap Inc.
The Snapchat data scientist interview questions will consist of many problems that test the full-stack knowledge of data science. This means that for the technical interview, Snapchat will test SQL queries, Python scripting, AB testing and experimentation, statistics, and product questions about Snapchat.
The interview process for a Data Scientist role at Snap Inc. is structured to assess both technical skills and cultural fit within the team. It typically consists of several stages, each designed to evaluate different competencies relevant to the role.
The process begins with a phone interview conducted by a recruiter. This initial screen lasts about 30-45 minutes and focuses on your background, experience, and motivation for applying to Snap Inc. The recruiter will also provide insights into the company culture and the specifics of the Data Scientist role. This is an opportunity for you to express your interest in the position and ask any preliminary questions you may have.
Following the recruiter screen, candidates typically undergo one or two technical interviews. These sessions are often conducted via video call and focus on assessing your proficiency in SQL and Python, as well as your understanding of statistical concepts. Expect questions that require you to demonstrate your ability to manipulate data, perform A/B testing, and analyze metrics. You may also be asked to solve case study problems that reflect real-world scenarios you might encounter in the role.
Candidates who successfully pass the technical screen are invited to an onsite interview, which may be conducted remotely in some cases. This stage usually consists of multiple rounds, often around four to five interviews, each lasting approximately 45 minutes. During these interviews, you will meet with various team members, including hiring managers, data engineers, and product managers. The focus will be on both technical skills and behavioral questions, assessing your ability to collaborate with cross-functional teams and your understanding of Snap's products and user experience.
In some instances, candidates may be required to present a case study or a project they have worked on. This is an opportunity to showcase your analytical skills, problem-solving abilities, and how you approach data-driven decision-making. Be prepared to discuss your thought process, methodologies, and the impact of your work.
The final stage may involve a conversation with senior leadership or a director. This interview is often more focused on cultural fit and alignment with Snap's values. You may be asked about your long-term career goals and how you envision contributing to Snap's mission.
As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during each stage, particularly those that assess your technical expertise and problem-solving skills.
Most of the behavioral interview questions are during the recruiter and onsite interviews. Common behavioral questions in the data science interview would be:
The technical screen for Snapchat data scientists will revolve around answering a data science product manager interview questions and a SQL question. Many times, these will involve scenarios involving ads and engagement.
They may also test more statistics and AB testing in the technical round if it’s on a team with lots of experimentation.
Here are some example technical questions:
Average Base Salary
Average Total Compensation
Snapchat has a high compensation package, which is worth knowing during the offer negotiation. Their average salaries for technical hires range from $193,342 (in the 25th percentile) to $273,544 (in the 75th percentile). The average salary is $237,743, while the top 10% earn $319,865. For this reason, the interviews are difficult, and the hiring baseline is extremely high.
A recent data scientist with five years of experience also got an offer at Snapchat that offered a $195K base salary with $280K of stock options plus a $20K bonus. This comes out to a yearly total compensation of almost $300K in just the first year.
Snapchat is also doing pretty well right now. They recruited a lot of good talent from Facebook, Google, Amazon, etc… companies, and engineering culture is also good. Given multiple offers, Snap can usually beat any other offer regarding total compensation in data science.
The company culture notes that the people in data science teams have good work-life balance and do not get stressed too often within project release deadlines. However, different teams can have different cultures, and there are horror stories about some data science teams at Facebook, which can be similar to Snapchat.
You may also check our resources on top data science companies to see which other firms are hiring for similar positions.