IN THIS ARTICLE
- What’s the work culture like at Google?
- What are the work benefits like at Google?
- How much does a data scientist make at Google?
- What’s the data scientist interview process like at Google?
- What’s the internship process for data scientists at Google?
- What's it like to work as a data scientist at Google?
- Springboard can help you get hired as a data scientist at Google
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Between its search engine business, productivity and communication tools, YouTube, mapping and travel services, and the Android operating system, Google is a playground for data science specialists.
With so many data-driven verticals, the company relies on data scientists to gather, process, and tease out business insights. Whether they’re identifying ways to make Google’s cloud platforms more efficient, helping the organization understand the usage of user-facing products, or simply using the company’s own data to help answer business questions and develop optimization methods, data scientists are a core part of Google’s business.
Few companies have had as transformational an effect on the world as Google. Read on to find out how to get hired as a data scientist at Google—and don’t forget to check out the guides below!
- Google Data Scientist Interview: A Complete Guide
- Google Data Scientist Internship: A Complete Guide
- How Much Does a Data Scientist at Google Earn?
- Day-in-the-Life of a Data Scientist at Google
What’s the work culture like at Google?
Google prides itself on its “Googleyness,” a term used to encapsulate its culture and the desirable qualities in its employees that contribute to a healthy and productive workplace.
“Are you intellectually curious? Do you work well in an ambiguous environment? Do you get excited by tackling a really big problem?” said Kyle Ewing, director of talent and outreach programs in Google’s People Operations Department. “That is the kind of person we know is the most successful here.”
Springboard mentor and Google data scientist Artem Yankov told Springboard that a few other “Googley” attributes that the company looks for include acting with the customer in mind, actively looking for opportunities to support your team, having initiative beyond your core work responsibilities, and participating in Google events such as training or recruiting.
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What are the work benefits like at Google?
Google is something of a model for technology companies when it comes to showering employees with benefits and perks. In addition to giving employees very few reasons to leave its campuses because it takes care of their meals, healthcare, and wellness, Google’s other employee benefits include:
- Complete health insurance coverage, including on-site access to physicians, physical therapists, chiropractors, and massage services
- Charitable giving matching
- 18-22 weeks of maternity leave
- Fertility assistance
- Adoption assistance
- Dog-friendly offices
- On-site fitness centers and health classes
- Complimentary meals
- 401K matching
- Personal and professional development support
How much does a data scientist make at Google?
The average salaries of Google data scientists depend on years of experience, education, and location, and the total compensation can vary greatly depending on whether someone qualifies for an annual bonus or stock grants.
At the most entry-level of the range, Google’s data science interns make around $7,500 a month, in addition to benefits such as a housing stipend and health insurance for the duration of their internship.
Data scientists who hold an undergraduate degree in a relevant field such as computer science, statistics, or mathematics, and have a few years of experience under their belt can earn around $142,147, in addition to cash bonuses and stock grants.
Senior data scientists who also hold master’s degrees or a Ph.D. in a related field, such as machine learning, and have more than five years of experience can make around $161,544, in addition to cash bonuses and stock grants.
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What’s the data scientist interview process like at Google?
Like many other major tech companies, Google’s interview process for data scientists begins with a phone interview with a recruiter where high-level questions are asked about an applicant’s background, interest in the company, and work experience.
During this phase, recruiters assess whether an applicant has the minimum qualifications for the role, including an undergraduate or advanced degree in computer science, statistics, economics, mathematics, bioinformatics, physics, or a related field. They will also assess an applicant’s experience with analytics, operations research, and advanced analytical methods.
Qualified individuals who make it through the initial phone screener typically move onto a technical screener where they are given an opportunity to show off their technical capabilities. Candidates can be expected to code in Python and SQL, explain their technical problem-solving methodology, and apply their statistical skills to real-world data.
The final round is an onsite interview loop where a candidate will be asked more technical questions, be tasked with performing statistical data analysis, answer situational questions about Google’s products, and make business recommendations based on hypothetical scenarios.
What’s the internship process for data scientists at Google?
Google offers internships across three categories: engineering and technology (software engineering, UX research and design, and data science), business (sales, marketing, communications, and HR), and BOLD, which stands for Build Opportunities for Leadership and Development—an internship program for undergraduate seniors from historically underrepresented backgrounds.
Internships are typically full-time, paid, and run for 12-14 weeks during the summer.To land a coveted Google internship, candidates need to succeed on two fronts during the application process: technical skills and “Googleyness.”
The former is about a candidate’s CV and qualifications: Are they currently enrolled in an undergraduate or Master’s degree? Do they have a background in computer science, statistics, computational biology, economics, or other technical fields? Are they proficient in scripting and database languages such as Python, Java, SQL, or MATLAB? Do they have experience or interest in the quantitative discipline?
The latter is about a candidate’s “Googleyness,” which relates to their attitude, work ethic, and whether they are the kind of person with whom others want to work and be around.
What’s it like to work as a data scientist at Google?
A Google data scientist’s day-to-day is largely determined by the product teams they’re on. While all of Google’s data scientists are skilled in SQL, Python, data processing, experiment design, performing original research, working with complex data sets, using statistical software, and developing data-driven hypotheses, they each apply their skills to different areas of the business.
For example, Google data scientist and Springboard mentor Artem Yankov works on Google’s forecasting team, where he uses data to help the company forecast how many customer service representatives it should hire globally to support all of Google’s products around the world and in multiple languages.
“I spend most of my time making sure that the data pipeline reflects the most current understanding of the business,” Yankov said. “If the data piece is in good order, then our forecast will be pretty accurate.”
Yankov describes the work culture as flexible and autonomous, with most days beginning with a brief team meeting before everyone is given time to work on the tasks of the day.
“It’s extremely flexible,” Yankov says of his work schedule. “At Google, most teams are spread internationally—I’m in Boulder, CO., but I have teammates in Ireland, Austin, and San Francisco. The team meetings are scheduled when it’s convenient for everyone to attend, and it’s flexible, so long as you get your work done.”
Springboard can help you get hired as a data scientist at Google
As both a data scientist at Google and a mentor for Springboard’s Data Science Career Track, Yankov said that Springboard provides the foundational elements needed to do the job.
“Springboard will introduce you to the whole process, from data extraction to putting out a model into the real world,” Yankov said.
In addition to teaching students to code and introducing them to the standard algorithms that are commonly used, Springboard’s data science bootcamp will also teach students to use tools such as Pandas to wrangle and clean data, conduct an analysis of datasets to identify stories and solutions, perform predictive modeling, and deploy machine learning algorithms.