Google is one of the most data-driven companies in the world. But what does that mean for its data scientists? Read on to find out more about the day-to-day responsibilities of a data scientist at Google.
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Google generates huge amounts of data in nearly every facet of its business, from the high volume of search results from its namesake search engine to location and communication data via its mapping and messaging software, to its broad-reaching advertising tools, to the Android operating system.
Because of this, data scientists play a pivotal role in the business—they use statistical methods and create data pipelines to forecast customer and company needs; they use machine learning to make search result recommendations; and they write algorithms to teach Google’s virtual assistant to understand voice-based commands.
Springboard mentor Artem Yankov is a Google data scientist who has worked in the field for nearly seven years. Below, he shares his experience and offers advice that can help future Google data scientists succeed in the role.
“Data scientist” is an umbrella term at Google under which data scientists on different teams exercise their areas of specialization. Artem Yankov, a Springboard mentor and Google data scientist, is on Google’s forecasting team and uses data to help the company predict how many customer service representatives need to be hired globally to support Google’s products.
Yankov’s day typically begins with a 15-30 minute team meeting where everyone discusses what they’re working on, before tackling the tasks of the day, which can range from updating data pipelines to make sure they reflect the latest information, reviewing his colleagues’ code before it gets deployed, and addressing any issues that stakeholders raise.
“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.”
Yankov is part of a team at Google that focuses on forecasting, which means he spends a lot of time on data science basics such as ensuring that the data pipeline accurately reflects the most current understanding of the business.
“If the data piece is in good order, then our forecast will be pretty accurate,” he said.
In addition to teasing out actionable insights from data, his role requires that he think critically, apply his coding knowledge to daily tasks, and find ways to communicate technical concepts to non-technical stakeholders, and vice versa.
On the critical thinking front, Yankov said that a lot of data science work is mechanical, and where a data scientist can bring value is in their ability to ask critical questions and find answers. For example, what happens to forecasting when the Covid-19 pandemic is factored into the equation? How can Google’s forecast take into account that the pandemic is a seasonal event, but is unlikely to recur in a similar pattern to other seasonal events? That’s when a data scientist steps in with their creative and critical problem-solving skills.
On the coding front, Yankov said that while data scientists don’t need to know how to code at a production level, it helps to understand code in any language (the most common of which are SQL and Python) because it’s useful in identifying data pipeline issues.
And on the communication front, Google’s data scientists often work alongside non-technical stakeholders, so it’s important to be able to explain technical concepts in layman’s terms, while also being able to translate and map feedback given in non-technical terms to algorithms.
“You’ve got to be able to talk technically to people who are not technical,” Yankov said.
In order to succeed as a data scientist—both in general and at Google—Yankov has three pieces of advice to share:
“The main thing is to not get fixated on algorithms and tools because they’re always changing,” said Yankov, who added that machine learning libraries are always changing and that no matter how well you learn an algorithm, a better one is likely to come along.
“It’s more about having a learning mindset. Just be curious and critical of everything—critical in a questioning way. Question why things are the way they are and try to find answers.”
“Getting a lot of hands-on experience will be more valuable to you in the long-term, and it’s more fun,” Yankov said. “You could read a textbook and learn theories, but they probably won’t stick with you. If you have a real problem you’re trying to work on, the pain you go through will ultimately stick with you longer.”
For data science newcomers, Yankov recommends getting hands-on experience through Kaggle, where real-world problems and robust datasets are shared with the public and anyone can develop their own models. He also recommends reading Kaggle’s forums because of the breadth of knowledge shared through the questions and comments posted by fellow data scientists.
Like most technology companies, Google evaluates its employees on their problem-solving abilities, leadership skills, and project execution. But it also prioritizes “Googleyness,” which has to do with what an employee brings to the company’s culture.
Some “Googley” attributes, according to Yankov, including 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.
“Being a Googler is incredible,” Yankov said. “People would be hard-pressed to find a better company to work for.”
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