Everyone has heard about the tech giant Google! Also, data science is evolving rapidly, capturing its breadth-taking position in all the different industries and the news headlines, of course. From finance to healthcare, every domain is leveraging data science, so as does Google. But Google is not just famous for its search engine, Gmail, Android OS, business strategy, and other products; it’s the playground for data scientists and data analyst professionals. But it is not a joke to get a job as a data scientist at Google. There are a lot of requirements and work experience that they will look forward to hiring you. In this article, you will get a quick walk-through on how to land a Google Data Scientist job in 2022.
Who are Data Scientists?
Data scientists are expert data science professionals responsible for analyzing the most complex data of the organization. They dissect and interpret complex data to support business executives take more reliable data-driven decisions. Data scientists should have profound knowledge of programming, statistics, mathematics (multivariable calculus and algebra), model machine learning algorithms, etc. They leverage clean data to identify hidden patterns and meaning by feeding these data into the ML model.
How does Google leverage the potential of data scientists?
Google entirely relies on data scientists to gather, analyze, and tease out business perspicacity. That could be making Google cloud efficient or helping the firm understand the user’s usage and behavior on any particular user-facing product. The data owned by Google are given to the data scientist and his team to help answer business questions and develop optimization methods. Google’s data scientists are the core members of Google’s business. A Google data scientist (entry-level) enjoys a salary of around $7,500 a month. With an experience of three to four years, the pay hikes up to $142,147 and gets $161,544 with more than five years of experience. So, getting a job as a Google’s data scientist is not a casual whim. There are a few significant points that an aspirant or professional must keep in mind.
Skills Required to Become a Data Scientist
Data scientists need a solid foundation in statistics and mathematics. Aspirants from different backgrounds and streams can cultivate themselves and learn the skills to become a data scientists. Understanding the data, its patterns, and extracting meaning from the granular data can pave the way to become a data scientist. Here are some of the basic requirements a Google’s Data scientist job need in the year 2022.
- Background and work experience: Google interview for the data scientist role begins with a phone call. In the telephonic round, the interviewer will ask your background and some high-level questions or project descriptions related to data science to check your inclination towards the domain. They also look for minimum qualifications for this role. That includes a graduate degree or advanced degree in statistics, computer science, bioinformatics, economics, physics, mathematics, or a related field. In Google, mostly the motivated, problem solver, passionate, and curious people get recruited for the data scientist role. The number of digits in the salary depends on the experience and previous projects you have worked upon with other organizations.
- Programming skill: Programming has become the most valuable skill a Google data scientist should have. Among the various programming languages, Python and R are the most widely used languages in data science. Python is a general-purpose programming language. It can handle everything from data analysis, mining, website development, running embedded systems, etc. Both R and Python have various libraries and frameworks for data science and machine learning libraries like Pandas, Seaborn, Dplyr, Shiny, Keras, TensorFlow, etc. Programming also helps design numerous statistical and regression models for analyzing data and extracting insights from granular data. A data scientist must have the skill to visualize the analyzed data so that business executives can make better decisions. Google’s interview team expects their aspirants to solve hands-on coding problems in Python and SQL after the technical screening round gets over.
- Database and database languages: To get a job in Google’s data scientist role, the professionals must have a sound understanding of database systems and database languages like SQL and NoSQL (MongoDB). Operations like CRUD and providing access to data points are some key concepts every data scientist should know.
- AI and ML: Those data scientists who stand out due to their true proficiency in artificial intelligence and machine learning get a better chance at Google. Google hires experienced ML and AI worshippers because ML and AI models can analyze large chunks of data reducing tedious tasks. Data-driven algorithms can automate a large part of the code and help the organization understand the meaning these large chunks of data are emitting.
- Business Strategy: Data scientists craving to join Google needs a clear understanding of the objectives of different products and business strategies Google uses. It will give you an upper hand over the rest of your peers, plus help you conduct better data analysis from a business standpoint. Data scientists have a lot of roles to play at Google. Such a mindset and knowledge of business strategy will help you slice and dice the data beneficial for Google.
Few other soft-skills require to become a Google’s data scientists are:
- Communication
- Networking
- Team collaboration
- Story-telling
- Self-learning
To make yourself eligible for hiring at Google as a data scientist, you need to keep yourself up-to-date with the latest updates at Google Careers. Also, grooming your LinkedIn profile and staying active on LinkedIn through posts and comments will make your profile visible. An informative textual post or infographics in the data science domain will also help boost your profile to a significant level.
Once you land yourself into the final round of the Google data scientist interview process, you will face some core technical questions on data structure, statistical data analysis, hypothetical scenarios, and ML algorithms. I hope this comprehension has given you a 360-degree idea of the various aspects of getting a Data Scientists role at Google.