Data science roles at Google are highly competitive and difficult to land. Fortunately, enough people have successfully gone through the Google data scientist interview process to share their experiences and offer valuable advice.
If you’ve graduated from a data science degree or bootcamp, put together an impressive portfolio, polished your programming and quantitative analytics skills, and have scored a coveted interview with a Google recruiter or hiring manager, you might understandably be nervous about what comes next.
Interviewing for any role can be an intimidating experience, but the best way to ensure success is to be prepared for what you’ll be asked and to know what the company is looking for. Current and former Google data scientists share their own interviewing experiences below.
What does a data scientist at Google do?
At a high level, data scientists at Google evaluate and improve the company’s products and services using statistical methods. In practice, this means bringing data analysis to every stage of the product development and deployment process, from the brainstorming phase to product creation and development, to identifying ways to improve on what’s already been built.
Regardless of the team they join or the projects they work on, data scientists at Google have a few things in common: most have backgrounds in computer science, economics, mathematics, statistics, or a related field; know coding languages such as Python, SQL, or Java; can deploy machine learning and artificial intelligence algorithms, and aren’t afraid of experimentation with different data structures and quantitative analysis methodologies.
With these skills as prerequisites, Google’s data scientists can specialize in different facets of data analysis depending on the team they join. For example, Artem Yankov, a Springboard mentor and Google data scientist on its forecasting team, helps Google determine how many customer service representatives should be hired globally to support its products and services both around the world and in different languages.
In order to achieve the most accurate forecasts possible, Yankov spends the bulk of his time ensuring that the data pipeline reflects the most current understanding of the business because if the data is off, then the results will also be off.
What skills does a data scientist at Google require?
In addition to having prerequisite skills in data wrangling and analysis, Yankov said that there are three additional skills that he frequently uses in his role: critical thinking, coding, and communication.
“A lot of data science work is more or less mechanical,” Yankov said. “Where a data scientist adds value is in asking critical questions and finding answers to them.”
When it comes to coding, Yankov said that data scientists at Google aren’t expected to code at a production level, but since Google employees can view the code behind the company’s products, it is useful for a data scientist to understand code in any language (particularly Python and SQL) so that they can diagnose issues in the data pipeline.
Communication is also an important and commonly used skill, according to Yankov, who said that data scientists often work with non-technical stakeholders, so it’s important to be able to explain technical concepts in an accessible way, while also translating feedback so that it can be mapped to technical solutions.
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What is the interview process for a data scientist at Google like?
Google’s data scientists report filling out an online application before going through a phone screening, a technical video conference screening, and then being invited onsite for an interview loop.
- The phone screener. Similar to technology companies like Facebook, Amazon, and Apple, the Google data science interview begins with a phone screening in which a recruiter asks basic get-to-know-you questions about former experience and why you want to work at Google.
- The technical screener. The technical screen usually takes place over video conference with a data scientist and is an opportunity for Google to assess your technical capabilities. During this stage, you might be asked questions about A/B testing, statistics, experimental design, and coding questions where you can show that you know how to code in Python and SQL. Some technical questions that have been asked in the past include: “For a sample size of N, the margin of error is 3. How many more samples do we need for the margin to hit 0.3?” and “Write a function to reverse a string.”
- The Googleyness screener. Google’s onsite interview typically consists of a loop of five interviews with people ranging from product managers to data scientists and business executives. In addition to being asked both technical and situational questions, this final round will assess your leadership qualities, how you navigate workplace ambiguity, and whether you’ll be a culture fit.
What happens after the interview process?
After an initial round of interviews in which recruiters and hiring managers ask about your background, interests, and experiences, you can expect to solve technical challenges using SQL, Python, and other quantitative analysis tools, as well as think strategically about Google’s products, services, and business opportunities.
On the technical front, you might be asked questions about designing a feature for Google Home and be expected to describe your method.
On the product strategy front, you might be given a hypothetical situation, such as “Google Docs usage has dipped by 10%—what methodology would you use to understand this dip?” Or, “Google is interested in acquiring a company—how would you analyze the metrics of the acquisition target to help Google determine whether it should go ahead with the purchase?”
Data scientists who have gone through Google’s interview process have said that the company asks a lot of situation questions about its products, so it helps to familiarize yourself with Google’s suite of products and services and to spend some time evaluating them because the company wants to see that you’re a holistic thinker.
Further reading: here is a guide with data science interview preparation tips to know what to expect from your data science interview
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