What Google Data Scientists Do on a Daily Basis

Here is what Google data scientists do on a daily basis

Google is one of the most data-driven companies in the world, with over 1 billion monthly active users across its various products and services. Data science is at the core of Google's business, as it helps the company understand user behavior, optimize performance, and innovate new solutions. But what do Google data scientists do on a daily basis? How do they use data to solve complex problems and create value for the company and its users? In this article, we will explore some of the typical tasks and responsibilities of a Google data scientist.

Data Collection and Processing

One of the main tasks of a Google data scientist is to collect and process large amounts of data from various sources, such as weblogs, user feedback, experiments, surveys, and external databases. Data collection and processing involve using tools and techniques such as SQL, Python, BigQuery, Dataflow, and TensorFlow to extract, transform, and load (ETL) data into a suitable format for analysis.

Data collection and processing can also involve ensuring the quality, reliability, and validity of the data, as well as dealing with missing, incomplete, or inaccurate data. Data collection and processing can take up a significant amount of time and resources, as data is often messy, noisy, and heterogeneous.

Data Analysis and Modeling

Another major task of a Google data scientist is to analyze and model the data to uncover insights, patterns, and trends that can help answer business questions and support decision-making. Data analysis and modelling involve using tools and techniques such as R, Python, TensorFlow, Colab, and Data Studio to perform descriptive, exploratory, inferential, and predictive analytics on the data.

Data analysis and modelling can also involve using machine learning, deep learning, natural language processing, computer vision, and other advanced methods to build and evaluate data-driven models that can forecast outcomes, optimize performance, or generate recommendations. Data analysis and modeling can require a high level of creativity, curiosity, and critical thinking, as data is often complex, ambiguous, and dynamic.

Data Communication and Visualization

A final and crucial task of a Google data scientist is to communicate and visualize the data and the results of the analysis and modeling to various stakeholders, such as other teams, senior management, or external partners. Data communication and visualization involve using tools and techniques such as Google Slides, Data Studio, Tableau, and D3.js to create clear, concise, and compelling reports, dashboards, charts, graphs, and other visual aids that can convey the key findings, insights, and recommendations.

Data communication and visualization can also involve presenting and explaining the data and the results to the stakeholders, as well as answering questions, addressing feedback, and providing suggestions for further actions. Data communication and visualization can require a high level of communication, collaboration, and storytelling skills, as data is often abstract, technical, and nuanced.