What does a Data Scientist at Google, Facebook, Amazon do?

Landing a successful Data Science role at a leading tech multinational is a dream breakthrough for almost all aspiring Data Scientists. 

If you’ve been wondering how does working at these companies look like, this blog post is the answer to just that!

We have taken three top companies of the world- Google, Amazon and Facebook and dissected what working at them like a Data Scientist is like.

Read on to understand the qualifications, roles and responsibilities and the career paths of Data Scientists who work with Google, Amazon and Facebook.

Data Scientist at Google

Data Scientists at Google are usually either product focused or analysis focused. Broadly, they can be divided into two categories:

1. Quantitative Analyst
These analysts work on tools and experiments like A/B experiments, statistical modeling & product research.

These Data Scientists work to change search algorithms, predict customer lifetime value (CLV), or estimate internet penetration in some country 5 years from now. 

They typically have advanced degrees in statistics, quantitative studies and mathematics.

2. Product Analyst

These Data Scientists work on analyzing everyday business issues using internal & external datasets. 

These Data Scientists work on issues around questions like which products are working where, why are the products working somewhere and not at another target market and what is the consumer sentiment around different products.

These Data Scientists have higher degrees in specialised subjects and a lot of domain knowledge.

What is expected of a Data Scientist at Google?

A Data Scientist at Google is focused on improving and examining the products at Google. Data Scientists work across teams with engineers and analysts on a wide range of problems.

The roles and responsibilities of a Data Scientist at Google include:

  • Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. – Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
  • Build and prototype analysis pipelines iteratively to provide insights at scale. Develop comprehensive knowledge of Google data structures and metrics, advocating for changes where needed for product development.
  • Interact cross-functionally, making business recommendations (e.g., cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
  • Research and develop analysis, forecasting, and optimization methods to improve the quality of Google’s user facing products.

You can read more about a Google Data Scientist here.

Data Scientist at Amazon

A Data Scientist at Amazon designs quantitative systems and forecasting models that generate multi-billion dollar predictions.

A Data Scientist at Amazon is looked upon as a problem solver who enjoys diving into data, dealing with difficult modeling challenges, and using strong communication skills to effectively communicate between technical and business teams. 

As a Data Scientist, you work across domains with economists and statisticians to produce modeling solutions, partner up with software developers and data engineers to build end-to-end data pipelines and production code, and build relationships with senior leadership to communicate results and provide scientific guidance to the business.

What is expected of a Data Scientist at Amazon?

  • Implement statistical methods to solve specific business problems utilizing code (Python, R, Scala, etc.).
  • Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.
  • Directly contribute to the design and development of automated forecasting systems.
  • Build customer-facing reporting tools to provide insights and metrics which track forecast performance and explain variance
  • Collaborate with researchers, software developers, and business leaders to define product requirements, provide analytical support, and communicate feedback.
  • Presenting critical data in a format that is immediately useful to answer questions about the inputs and outputs of Forecasting systems and improving their performance.

You can read more about Amazon’s hiring process here.

Data Scientist at Facebook

There are multiple roles for Data Science at Facebook as well. 

To pick out what’s the common understanding behind these roles and what are the qualities being looked for by hiring managers might be a heavy generalization but this is what we have to focus on, for the sake of the article.

Facebook maintains that their larger goal is to work with Data Scientists who suggest data-backed insights which will result in structuring the product road-map or move key metrics that the product teams track. 

Data Scientists at Facebook build infrastructure that is used by other Data Scientists and engineers. 

Like every other product company, Data Scientists work closely with Engineering and Product teams.

What is expected of a Data Scientist at Facebook?

  • Analyze and design experiments to optimize product features or move key metrics
  • Data mining/analysis to present business opportunities to pursue or product feature suggestions or sometimes to understand metric movements.
  • Building production Machine Learning models 

You can read more about what a Data Scientist job looks like at Facebook here.

This is what an average Data Scientist at world-class companies works on.

To further elaborate on what Data Scientists do universally, here’s a list of tasks:

  1. Collecting a large amount of raw data and categorizing it
  2. Solve business-related problems with Data-focused methods
  3. Work with various languages like R, Python
  4. Understand statistics and statistical models
  5. Read on Machine Learning, NLP on a regular basis
  6. Finding patterns in data
  7. Developing new algorithms to analyze data
  8. Finding bugs in the previous iterations of models

These are some of the tasks that you as a Data Scientist have to handle on a regular basis and develop efficient and innovative methods to help your organizations derive the maximum insights into collected data. 

Data Science Compensation 

India is the 5th largest economy in the world and its GDP has crossed $3.202 trillion and aims to reach $5 Trillion in the coming years. Indian economy is growing despite the uncertain world financial climate and Data Science will benefit most from it. Industries like Fintech, energy, pharma/healthcare, e-commerce, Print & electronics Media, and retail commerce are the sectors that will be creating the largest number of jobs in this field with average salaries ranging from 10 to 14 LPA and chances of bagging high salary is a good possibility. 

According to Business Insider, LinkedIn data, India ranks among the top three countries poised to lead in AI Automation. Expertise in Spark and Python will help you get the Best Salary in the industry.

Obviously, the opportunities are endless. A Data Scientist can find himself involved in multiple domains with a lot on their plate most times than not!


To know more about how an average day in the life of a Data Scientist looks like, click here!

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