Google is an American technology giant that specializes in Internet-related services and products including online advertising, search engines, cloud computing, software, and hardware. The company was founded in 1998 and headquartered in Mountain View California .
With its rapid growth since incorporation in 2002, Google has developed a wide range of products, acquired a long list of companies, and entered into mainstream culture through its dominance in search. Now Google has branched into tons of products and services such as office suite apps, email clients, cloud computing, video chat, android, and tons more.
With the plethora of products and services offered by Google and the staggering number of users, one might ask just how much data does Google handle? Based on 2019 statistics, Google processes over 40,000 search queries every second on average, which translates to over 3.5 billion searches per day and 1.2 trillion searches per year worldwide . To Google, this presents endless opportunity to help its customer grow and scale, and to data scientists this present a treasure trove of information for analysis and interpretation to help identify opportunities for Google and its clients, and shape Google’s business and technical strategies.
If you’re interested in what’s asked on the interview, check out the list of Google data science interview questions!
The Data Science Role at Google
Data scientists at Google work across a wide facet of teams, products, and features, from enhancing advertising efficacy to network infrastructure optimization.
The Google data science role is primarily an analytics role that is focused on metrics and experimentation. This is distinctly different from the machine learning and product analyst roles that also exist at Google that focus more on the engineering and product side respectively. The data science role at Google used to be called a quantitative analyst before switching to data science to attract more talent.
Google usually only hires experienced individuals with at least two to three years of industry experience in analytics or related fields. Google does have programs for internships and university graduates in data science, and specifically has more advanced roles for new PhD graduates.
Other relevant qualifications include:
- Masters or PhD in Statistics, Computer Science, Bioinformatics, Computational Biology, Engineering, Physics, Applied Mathematics, Economics, Operations Research or related quantitative discipline, or equivalent practical experience.
- Advanced experience in statistical software (e.g MATLAB, Panda, Colab, S-Plus, SAS, etc.), programming language like Python, R, C++, and/or Java, and advanced experience in database language (e.g SQL) and management systems.
- Experience with big data and cloud platforms to deploy large-scale data science solutions.
- Experience in data science with a focus on business analytics, designing and building statistical modelling, visualization, machine learning, digital attribution, forecasting, optimization, and predictive analytics
- Knowledge of Advanced Statistical Concepts and applied experience with machine learning on large datasets.
- Demonstrated problem-framing, problem-solving, project management, and people management skills.
Data Science Teams at Google
From the marketing department to the research team at Google Research, data scientists leverage advance analytics, machine learning theory, and statistical concepts and methods to identify opportunities for product development and improving customers experience.
The most common teams that data scientists at Google land in are:
- Engineering and Design: Apply advanced analytics and build analysis pipelines iteratively to provide insights at scale while collaborating cross-functionally with various teams to provide business-impact recommendations.
- gTech Professional Services: Leveraging technical implementation, optimization, and key solutions to help client customers attain their business goals on advertising.
- Google Maps Core Metrics: Develop core metrics and experimentation practices that define how engagement, adoption, and retention for Google Maps is measured and tested.
- Geo: Apply advanced analytics tools on large geo dataset, build and prototype analysis pipelines, research and develop analysis, forecasting, and optimization methods, and make business recommendations at various levels.
- Operations and Support: Partner with engineers to analyze, interpret data, and develop metrics to measure results and integrate new tools into customer support and operations.
- Ads: Build and expand Google’s advertising capabilities by leveraging statistical research and machine learning concepts.
- Search Ads: Design and analyze complex experiments to understand the effect of changes to the system and provide recommendations for improvement. Collaborate with analyst and software engineers (SWE) on core algorithms to improve customer experience.
- Business Strategy: Provide meaningful recommendations on strategy by collaborating with cross-functional teams to understand their business needs.
The Google Interview Process
The interview process at Google starts with a recruiter first reaching out to you with a prescreen questionnaire via email after your application. Then you’ll get a phone call from a recruiter discussing interests and experiences. After this interview, the recruiter will then schedule a phone screen interview with a data scientist or a hiring manager which is usually 45 to 60 minutes long. Once you finish through this part, an onsite interview comprising of five separate interview rounds will be scheduled.
The initial screen is usually a 30 minute phone call interview with a recruiter where they describes the job position, responsibilities, and then examples of different teams at Google, before then asking if you would like to pursue the data science position. In this interview, you get to chat with the recruiter and the recruiter learns more about your skills.
The recruiter is mainly trying to figure out your career goals and see how they align with Google’s culture and values along with the different teams that they can place you in.
Google’s data scientist technical screening is done via video conferencing (Google Hangouts) with a data scientist. This interview revolves around experimental design, statistics, and a probabilistic coding question. It also involves more technical discussions focused on past research and work experience and dives into what problems you faced and your technical approach to solving them. Try medium level questions on Interview Query to practice.
- Describe a past data science based project.
- What problems did you encounter? What approach would you have used if the data was different?
- A statistics question (computational stats) or causal inference question
- Coding question on a shared code editor
The Google Onsite Interview
The onsite interview is the last stage of the Google data scientist interview process. It comprises of 5 one on one interview rounds with data scientists covering computational statistics, probability, product interpretation, metrics and experimentation, modeling, and behavioral questions. Each interview lasts for approximately 45 minutes and there’s a lunch break in between.