Becoming a data scientist is pretty hard as you need to have tons of knowledge, technical skills, experience, and analytical skills. The more the data, the more complicated the job becomes for the person, but also rewarding. One of the most revered among these people is the Google data scientists.
Google is one of the largest and most well-known organizations in the world. They handle a major chunk of the data that travels across the world. That puts them in a unique position to manage one of the largest data banks in the world.
Being big data themselves, Google hires a lot of data scientists for a majority of reasons.
In this article, we’ll go over what a Google data scientist does, including their roles, responsibilities, and more.
Let’s dive right in.
What is the Role of a Google Data Scientist?
The Google data scientist role cannot be summarized into one definition. It’s crucial to distinguish all the roles a Google data scientist can take.
It’s the same with other data-heavy companies like Amazon, Facebook, Microsoft, or LinkedIn. Either the data scientist role is broken down into different job titles, or each data scientist is provided a specific focus and department.
The following are the roles of Google data scientists, followed by additional roles that come under the umbrella.
In general, data scientists have to analyze a lot of product data to determine what’s working, why it’s working, and how it can work even more. Similarly, Google data scientists are tasked with doing that for all Google products.
Now, Google has a lot of products, including Google Search, Android, Ads, and tons of others. Each product/service is absolutely massive and requires constant analysis, management, and overseeing.
That’s why a lot of Google data scientists end up assuming the role of a product analyst. One data scientist isn’t given more than one product because each product itself has so much data to analyze and numbers to crunch.
Google has an entire team of data scientists dedicated to each product just for the sake of data analysis.
Each day, these data science teams analyze hundreds of internal and external datasets. The reasoning behind it is usually weeding out business issues, productivity issues, improving efficiency, and improving the consumer experience.
Here’s a few things that are regularly analyzed.
- The products that are working the most.
- Where exactly are the products working?
- How is the overall response? What’s the reasoning behind it? Is it some specific campaign or feature, or is it something else?
- Target markets that are doing well.
- Why one product is successful in one target market and not another.
Using data analytics, data scientists answer the questions above and more each month (each week in some cases).
Since Google receives a huge amount of numbers and metrics, many data scientists are tasked with quantitative analysis. These quantitative analysts use their talents for business recommendations, reporting, and more.
For a little more detail, they do the following.
- Develop business recommendations for upper management using quantitative data.
- Create industry-wide reports for upper management and consumers.
- Work on getting appropriate data for any search algorithm changes.
- Estimate things like internet penetration, search engine usage, OS usage, ad spend, and more.
- Using machine learning models to predict consumer behavior based on quantitative data.
- Predict customer lifetime value where Google is directly selling a product or service.
All of these things tend to require specific tools and expertise. That’s why for this role, a Google data scientist usually needs to have a history with statistics, mathematics, and quantitative methods.
Furthermore, they usually work with tools like machine learning models, A/B experiments, forecasting, statistical modeling, various APIs, and more.
Keep in mind that no single data scientist works on one project. Google always assigns entire data science teams to work on a project. One reason is that there’s a lot of data, and another reason is that a team of analysts will end up with better and more accurate results.
Additional Google Data Scientist Roles
The roles above are how Google usually differentiates its data scientists. However, there are further classifications for careers in data science.
They include the following.
- Data Analysts – Simple data analysts, are tasked with making sense of large datasets, identifying trends, and coming up with conclusions. They help drive business decisions and back up other data scientists.
- Data Architects – The data architects are responsible for designing, creating, and managing the organization’s data architecture. For example, several data architects are responsible for managing the Google cloud system.
- Business Intelligence Specialists – These people are responsible for identifying key trends in datasets. They help develop insights for upper management to ease the decision-making process.
- Data Engineers – They tend to clean, collect, aggregate, and organize data from various sources. They then transfer said data to central databases using relevant database languages.
- Research Scientists – These research specialists work with software engineers, data houses, and other experts to complete unique research for organizations.
The data scientist job at Google usually has one of these roles assigned, along with a specific project or product.
Responsibilities of a Google Data Scientist
The responsibilities of a Google data scientist vary depending on their role. For example, a data scientist (engineering) working at the Mountain View headquarters will have different responsibilities than a data scientist (research) working in the New York office.
Similarly, an onsite data scientist will have more responsibilities than a remote data scientist.
However, there are a few responsibilities and things that are expected from every Google data scientist, including the following.
- Work with complex datasets to solve non-routine analysis problems.
- Apply analytical methods to conduct analysis, including data gathering, requirement specification, processing, managing deliverables, and presenting.
- Develop prototype analysis pipelines that can churn out insights at scale.
- Understand Google data structures and metrics to advocate for any relevant product development changes.
- Research various analysis, forecasting, and optimization methods to improve product quality.
- Communicate cross-functionally by making large-scale business recommendations (using cost-benefit analyses, experiment analyses, and more).
- Develop data visualization methods to relay findings to relevant stakeholders.
- Make use of professional services to gather the relevant data needed for new studies.
- Initiate and manage the discovery process by asking relevant questions.
- Acquire data, process it, and clean it for further integration and storing.
- Conduct data investigation and exploratory data analyses. Develop a process or model to do so, depending on your role and department.
- Make use of statistical modeling, artificial intelligence (AI), and machine learning for better results.
- Practice various optimization techniques to make processes more efficient.
- Make changes to the process and method of data collection, analysis, and presentation, depending on the feedback received from upper management and consumers.
Every Google data scientist is involved in operations research and analysis. They have equivalent practical experience with all of the above tasks, duties, and responsibilities. However, additional responsibilities do pile up based on your role.
Essential Skills of a Google Data Scientist
As a Google data scientist, there are a few must-have skills. These skills are something that all data scientists are expected to have, but Google has a unique perspective to it. As long as you can show what you can do with your skills, you don’t necessarily have to show a certification or degree to back it up.
However, a degree or certification most certainly helps.
Moving on, the following are some of the technical and soft skills that each Google data scientist is expected to have.
- Complete knowledge of computer science, including software engineering, database systems, artificial intelligence, numerical analysis, and interaction analysis.
- Expertise with various programming languages, including Python, Java, SQL, and R. The data scientist should be able to write complex computer programs to analyze massive datasets. That means writing a lot of code efficiently and accurately.
- Machine learning is a crucial part of data science because it’s used to develop predictive statistical models that use algorithms and logarithmic methods to automatically learn from data.
- Statistical analysis is crucial because it helps identify patterns in massive amounts of data. That means the data scientist should also have an eye for anomaly detection and pattern detection.
- Data visualization and storytelling are a major part of being a data scientist because you need to be able to communicate trends and insights.
- Communication and interpersonal skills are needed.
- Critical thinking and problem-solving play a major role.
- Analytical thinking is crucial to find analytical solutions.
- Business intuition is required to make better research decisions.
- Leadership skills are needed for self-direction and to head others toward success.
The skills listed above are required by Google for all data scientists. However, depending on what role you have and what project you’re working on, Google may require you to have additional skills.
Qualifications of a Google Data Scientist
The preferred qualifications of a Google data scientist may vary depending on where you’re applying. For example, at the Mountain View headquarters, Google may hire a data scientist without a college degree if they have the relevant work experience and skills.
However, generally, Google expects the following qualifications from their data scientists.
- A Master’s degree in any quantitative discipline like statistics, computer science, mathematics, physics, operations research, economics, bioinformatics, computational biology, electrical engineering, or industrial engineering. Any equivalent practical experience may work in some cases.
- A PhD in any quantitative discipline or related field is welcome but not necessary.
- At least two years of experience in data science, data analysis, project management, or statistical inference is needed.
- Experience with statistical software is required (Python, R, MATLAB, pandas).
- Experience with database languages like SQL is required.
- Understanding of linear models, sampling methods, multivariate analysis, and stochastic models.
- Working knowledge of machine learning and artificial intelligence.
- Certifications in data science and any of its components are not required but are preferred, especially if you don’t have a degree in any quantitative discipline.
Google is always changing the qualification requirements for most of their jobs, so it’s possible that the Google data scientist’s qualification requirements may also change.
What is the Average Google Data Scientist Salary?
Data scientist salaries tend to vary depending on the city and organization. On average, a data scientist in the United States earns around $114,534. The average range is between $81,000 and $162,000.
The average Google data scientist salary in the US is $146,467. The average range is between $136,000 and $256,000. The additional average pay is around $24,367.
Furthermore, Google data scientists can earn up to $61,473 in stock bonuses annually. The highest Google salaries are in Mountain View, New York City, Seattle, San Francisco, and San Jose.
Other than that, Google provides a lot of employee benefits, including healthcare, 401k plans, free lunches and snacks, gym memberships, pet insurance, student loan debt repayment, tuition assistance, and more.
The data scientist job is an extremely high-paying job, especially in high-profile companies like Google that deal with massive amounts of data.
What’s good about Google’s recruiters is that asking about salary expectations isn’t part of their data scientist interview questions. That means you are bound to be paid at least the lower range, and your qualifications and experience determine how much more you’ll be paid.
Becoming a Google Data Scientist
If you’re wondering how to become a data scientist, then you should focus on the fundamentals first. Once you have that down, you can focus on becoming a Google data scientist.
Every data science interview tends to lean toward why you went into data science. It’s crucial that you have a reason and a passion for it so you can describe your reasoning. Google considers this to be a major factor when interviewing candidates.
Once you have it all down, update your data scientist resume to reflect your efforts. Write a kickass cover letter and apply for that Google data scientist job you’ve been meaning to go for.