Google handles large amounts of data in order to create their products and services, and so requires both large data centers and many data professionals. Hence, data scientists are among the few professional tracks that Google truly values. There are numerous roles within data science that are core to Google’s portfolio of operations.
Google hires data scientists to support services and products that rely on user data, including search results, personalized advertisements, and traffic data for maps. They hire data scientists to write the algorithms to support these services and to come up with ideas to leverage data in new ways.
This article will discuss what Google does as a company, why data scientists are necessary to their operations, and what kinds of roles are available for data scientists at Google and in general.
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What Does Google Do?
Google handles more than 70% of online search requests worldwide and offers more than fifty internet services, from email to software to document creation and editing. Google also collects data and plays a vital role in many businesses in analyzing their customers’ traffic and behavior.
To better understand how and why Google does what it does, it’s helpful to look at how the company started and why they expanded in the ways that they did.
The History of Google
Google co-founders Larry Page and Sergey Brin met at Stanford University, when Sergey, a student there, was asked to show Larry, a prospective graduate student, around the campus. A year after this meeting, they developed a partnership building the first version of Google from their dorm rooms.
Essentially, their search engine worked by ranking websites according to the number of other pages on the internet that linked to them. Where other search engines would simply return a list of sites depending on how frequently a keyword appeared, Google would list results based on ranking, as well as mentions of the keyword.
Their invention caught the attention of Silicon Valley investors, family, and friends, and they ultimately raised about $1 million in starting funds. This allowed the team to move from Stanford to Menlo Park, California, where they worked from a new employee Susan Wojcicki’s garage.
By 1999, Google had received $25 million in funding, then saw massive growth in 2000 when Yahoo! incorporated the Google search engine into its site. By 2004, Google was processing 200 million searches a day, and by 2011, the daily search count reached three billion.
Google was forced to build 21 data centers around the world to house servers that could process these huge amounts of information. By some estimates, Google has probably several million computers linked together, powering these data centers.
Throughout the 2000s and beyond, Google continued expanding to include related services, some through the acquisition of other companies and some through research and development.
- Gmail: a free web-based emailing service
- Google Books: made library holdings available on the internet
- Google Earth: let users find satellite images of most locations on Earth
- Google Video: allow users to upload and download video content
- Android operating system
- Google Apps: including Google Calendar (a scheduling program), Google Talk (an instant messaging service), and Google Page Creator (a web page creation program)
- Google Docs: allow users to create and share documents
- Google Chrome: a web browser
- Google+: a social networking site following the failure of the social networking site Google Buzz in 2010
- Google Translate: speak, scan, draw or type words to translate them into another language
- Google Maps: get real-time traffic information and directions, and explore
- YouTube: watch audio and video
- YouTube Music: a music streaming service
- Chromecast: stream audio and video from your phone to your TV
- Google Play: watch movies and TV
- Messages: text messaging service
- Google Duo: video calling
- Google Chat: message with your colleagues
- Google Photos: store and edit photos
- Contacts: an organized address book
- Keep: save notes, lists, and voice memos
- Docs: create, edit and share documents
- Slides: create, edit and share PowerPoint presentations
- Sheets: create, edit and share spreadsheets
- Drive: store and share files
- Google Ads: appear as a Google advertisement in search results
- Adsense: monetize online content with Google advertisements
- Google Analytics: track use of your website
- Google My Business: register your business for search and maps listings
How Does Google Use Data?
These days, Google uses data not only to support its own business needs and services but also to serve the business needs of its many clients. Google collects information from any and all websites that use AdSense, a Google advertising service, Google Analytics, or Youtube video embedding.
This information includes the URL of the page that was visited and the person’s IP address using the site. Google also sets and reads cookies, which are used to track your behavior over time based on your activity while using a particular browser. This allows Google to remember your preferences between sessions.
Collecting this data allows Google to understand how effective advertising is, personalize content (including advertisements), and improve its services, as well as perform a number of other functions fueled by big data.
Google collects even more information from the way that you use Google services. This includes but is not limited to the things you search for with the search engine, the locations you use in Google Maps, the videos you watch on Youtube, the emails you send through Gmail, and your personal information used to register accounts, as well as your added contacts.
By storing and analyzing this data from many users at once, Google can provide sophisticated data-based services. For example, Google Maps can assign routes to drivers based on current traffic patterns, based on location data gathered from many drivers at once.
What Do Data Scientists Do?
Data scientists use statistics, mathematics, and computer programming skills to store large amounts of data, analyze it, and create processes for modeling the data appropriately. They are highly sought after professionals and require expertise in a number of different fields, especially computer science.
Data scientists work closely with businesses, creating algorithms and models that will find and show stakeholders what to expect from their customers, opportunities, and other important areas. They work on data projects from start to finish, establishing the first questions, and creating the mathematical functions that will produce the right results.
Data scientists are necessary anywhere that big data is being used, and so are very valuable for tech companies looking to make use of customer data.
Data Scientists vs. Data Analysts
There are many in-demand roles for data professionals, and their titles can sometimes be confused. For example, data scientists and data analysts actually have very different roles, even though they are often assumed to be the same profession.
In general, data scientists have more technical work than data analysts, creating processes to analyze and model data rather than simply observing and finding patterns in datasets.
Skill Requirements for Data Scientists
- Statistics. Data scientists should easily be able to spot statistical trends and outliers in a dataset.
- Machine learning. Data scientists should be able to create mathematical algorithms that allow a computer to learn from new data.
- Computer science. Data scientists should have a deep theoretical and practical understanding of how computers work, understanding the concepts of software engineering, database systems, and artificial intelligence, among other related topics.
- Coding. Data scientists should be fluent in multiple coding languages, especially those used to analyze and model datasets. Common language requirements include Java, R, Python, and SQL.
- Data communication and visualization. Data scientists need to be able to communicate with non-expert audiences about their findings, both verbally and visually.
- Business sense. Data scientists need to be able to intuitively understand the needs of a business in order to ask the right questions.
- Analytical thinking. Data scientists should have a naturally analytical mindset towards problems.
- Critical thinking. Data scientists should naturally take into account relevant objective facts before reaching a conclusion.
- Inquisitiveness. Data scientists should be naturally curious and capable of interrogating a dataset.
- Interpersonal skills. Because data scientists need to communicate their findings, it is important to be able to work well with many kinds of people.
More About Machine Learning
Machine learning is an increasingly big part of what data scientists do, especially at major tech companies.
Basically, machine learning is a kind of artificial intelligence, a way for a computer to change how it functions based on new data that it takes in. A machine learning algorithm will train itself based on patterns that it finds and make better predictions based on those findings.
For example, an app that makes music recommendations will have a certain method for deciding what suggestions to make based on a user’s interests. And as the algorithm takes in more information, it will update those suggestions based on any patterns or changes that it finds.
Machine learning makes using data more powerful and more efficient and is a very important tool for data scientists, so much so that understanding how to program a machine learning algorithm is essentially a requirement of the profession.
Can a Data Scientist Work at Google?
Data scientists are in very high demand at Google, as well as at other major tech companies. In fact, Google ranked as the top sixth employers of data professionals in 2019, making a list alongside Amazon, Apple, and Facebook.
Data scientists at Google bring home an annual salary of $139,607, on average, significantly higher than the industry average of $113,309 per year. The highest reported salary for a data scientist at Google is $226,898.
The Google Hiring Process
Google is growing faster than ever and employs over 114,000 people as of 2019, up from 25,000 employees in 2010 and only 300 employees in 2001. Seeking top performers in technology and business, Google has a multistep, unique hiring process for candidates.
Although it starts like most with a response to a job posting or and the first conversation with a recruiter, the interview process then follows a group decision-making standard, unlike a traditional interview. This process is meant to reduce bias due to the diverging opinions that occur within a team.
This means that the hiring process at Google is slow and deliberate and that a person is unlikely to be hired because of a personal connection to one of the existing employees. Hiring committee members generally represent a particular department and rotate in and out of the committee every three to six months.
Google also incorporates technical interviews to test candidates, but not your typical brain teasers. Rather, Google prefers to ask high-level questions about computer science and programming that practical test skills a candidate would need on the job. These topics are covered in the Technical Resource Guide that Google provides for students.
Google is unique because they prefer to hire versatile candidates who can switch from role to role, even within technical fields. The needs of the company shift frequently, and so they prefer to hire people who are generally good problem-solvers and are very intelligent.
The ability to perform high-level data analyses and coding tasks are important for a data scientist who wants to work for Google, but any candidate should be prepared to be tested not just for their role-specific skills, but also for their general understanding of how and why Google uses data and an ability to adapt to emerging business needs.
Google is an active recruiter of students, hosting outreach events at hundreds of universities across the world with an emphasis on hiring and training recent graduates. The company also provides information for students hoping to work for Google at their ‘students’ site, covering everything from resume advice to coding interview practice.
If you are interested in working at Google, check out the Google Careers site to search for open positions based on location, skillset, degree, or job type.
What It’s Like Being Trained at Google
Research shows that employees fail to retain 90% of what they learn during training at most companies. Because of this, Google developed an innovative strategy for training simple enough that it can be replicated by even the smallest of businesses.
This training strategy assumes that the best way to learn is by not just receiving information, but by receiving guidance and nudges from a supportive manager during the process of applying new information in daily work.
Managers are given “whisper” emails, which offer suggestions for managing techniques when integrating a new employee, either for one-on-ones or for team meetings. This promotes psychological safety, a necessary component of an effective learning environment.
What It’s Like to Work at Google
Google employees are supported mentally and emotionally throughout their time with the company with a number of different personal and professional benefits.
- Support for families. Google offers general parental leave benefits, retirement plans, death benefits, and more.
- Healthcare. Google offers excellent healthcare choices, as well as, onsite services such as doctors, physical therapists, massage therapists, and fitness centers.
- Nutrition. Google employees are served nutritious meals and snacks throughout the day from paid-for, onsite cafes, and kitchens.
- Donation matching. Google pledges to match charitable donations made by employees, including donations for volunteer hours.
- Personal time. Google offers generous vacation policies to allow for visits to family and friends, volunteer work, or any other personal needs.
- Financial support. Google matches retirement savings and offers financial advisors for employees, as well as other financial planning resources.
- Education. Google offers a wide range of educational opportunities for employees, from professional development skills like coding to personal development skills like music or cooking.
Google employees also benefit from getting to know the many talented people employed by the company, and by the opportunity to participate in a highly intellectual and creative hive mind. The driven and innovative atmosphere is contagious and seems to bring out the best in employees.
Here’s a video link on what is it like to work as a Google employee:
Employees are also encouraged to spend 20% of their time on the job on passion projects related to the company’s interests, a great example of Google’s focus on fostering creativity and entrepreneurship.
Other Major Employers for Data Scientists
Computer and information scientists, including data scientists, are employed heavily by the federal government and technology companies. There is also a high demand for data scientists in research and development fields, as well as in the software publishing industry.
However, data scientists can find work at many different kinds of companies, not just with these major employers. Employment of data scientists is expected to grow by at least 19 percent by 2026, much higher than average employment growth.
Here are the top 10 employers of data scientists in 2019:
- Fidelity Investments
There remains a heavy gender imbalance in these data-related positions, with a 3:1 male to female ratio across all positions. Just 23% of these data scientists were female, and just 18% of data scientists in the United States are female. This reflects a general trend of women being outnumbered and underpaid in positions related to technology.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Data scientists perform many functions that are necessary to Google’s operations, and Google seeks out the top talent in data science to keep their company functioning and to develop the next, innovative way to use data. Being hired at Google is difficult, but data scientists are very in demand, and if you are willing to put in the necessary efforts, making your way into this dream job is very much possible.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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