What does a data scientist do?
A data scientist analyzes and models data to generate business insights. Responsibilities, technical skills, and how the role differs from other profiles.
Looking for the right professional to join your data team? In this blog post, we’ll explain what a data scientist does and what role this professional plays in your company. In a 2017 article, the American magazine The Economist published an article discussing the rise of a new commodity that would be even more valuable to the global economy than oil: data. Now, six years later, that prediction is already part of companies’ reality, the rules of the corporate game have changed, and the market has already adapted.
Data is no longer part of a distant and remote future. It is possible to do business without the strategic use of data, but companies that don’t do this—and don’t even plan to—are leaving a lot of money on the table. This reality will only become more present in day-to-day operations. For this reason, it is essential to have professionals on your team who can handle this demand efficiently, and the Data Scientist can play a highly strategic role.
The role of data scientists in data teams
In a survey available on Statista, in 2021, the average number of Data Scientists in companies rose from 28 to 50. And this is no coincidence… In the context of the COVID-19 pandemic, in 2020, there was a major need to find modern and analytical ways to drive results. New innovations were implemented, many tests were run, and this professional’s role gained strong visibility in the market.
A data scientist is the professional capable of extracting useful information and generating insights from data, using advanced knowledge of programming, statistics, and data analysis to support data collection, analysis, and interpretation. In an organization, generally speaking, a basic data team is usually made up of engineers, analysts, and data scientists and is responsible for managing and analyzing company data with the goal of generating actionable insights, determining how data will be collected, stored, and analyzed, in addition to identifying which implementations are necessary so the company culture supports data usage.
Within this team, a data scientist fulfills a very important function, as these professionals have both technical and business knowledge, are responsible for understanding the key issues within the company that need to be solved, and also know how to find the data needed to locate, manage, and analyze large volumes of structured and unstructured data.
Other important responsibilities include:
Data collection and processing;
Exploratory data analysis, including visualization to identify patterns and trends;
Statistical modeling;
A company’s data maturity reports;
Hypothesis development and testing;
Development;
Model testing and optimization;
Model deployment.
When we think about a business context, because they are in direct contact with this information, they are also responsible for identifying organizational improvement opportunities to support the company’s data strategy and generate ROI (return on investment), suggesting improvements such as boosting data literacy and proposing actions that improve communication across teams, while also collaborating with one or more teams (for example: marketing and product) to support efficient data use across the organization.
Some important skills that a good data scientist should have are:
Programming: Currently, data scientists need deep knowledge and must know how to work with R and SQL, in addition to programming languages such as Python.
Statistics: Since they will be in direct contact with data, this professional must have strong knowledge of statistics, including theory, hypothesis testing, regression analysis, and data visualization.
Strong communication: This professional is more capable than anyone of identifying improvement points that need to be implemented within the organization, so good communication is essential, especially when creating reports and documentation.
The role of a data scientist can be highly strategic, as they play a very important function working with business data, in how it is used, with the aim of increasing ROI, in addition to helping implement a more data-driven culture within the company.
The data field is quite broad, and in some industries this professional’s functions may vary and have a greater or lesser decision-making role, but their contribution is always essential. At the start of your data-driven journey, they help implement processes and techniques for data processing. As your company evolves, they can extract more insights and generate value in a more agile and practical way from data.
If your company is not yet ready to bring one of these professionals onto the team, there are very simple and effective ways to outsource the hiring of data teams by contracting hourly packages from more experienced data companies, which provide technology, know-how, and a complete team to help your company take its first steps on its data-driven journey, ensuring access not only to data scientists, but also to experienced analysts and engineers.
When it comes to extracting value from your data, relying only on a data scientist is often not enough to meet all your company’s demands, and at that moment being able to count on a strategic partner that can provide a complete data team is also important.
Erathos also offers a data squad as a service, providing a complete team of specialists aligned with the market’s top practices and latest developments to deliver solutions that are most aligned with your business.
Looking for the right professional to join your data team? In this blog post, we’ll explain what a data scientist does and what role this professional plays in your company. In a 2017 article, the American magazine The Economist published an article discussing the rise of a new commodity that would be even more valuable to the global economy than oil: data. Now, six years later, that prediction is already part of companies’ reality, the rules of the corporate game have changed, and the market has already adapted.
Data is no longer part of a distant and remote future. It is possible to do business without the strategic use of data, but companies that don’t do this—and don’t even plan to—are leaving a lot of money on the table. This reality will only become more present in day-to-day operations. For this reason, it is essential to have professionals on your team who can handle this demand efficiently, and the Data Scientist can play a highly strategic role.
The role of data scientists in data teams
In a survey available on Statista, in 2021, the average number of Data Scientists in companies rose from 28 to 50. And this is no coincidence… In the context of the COVID-19 pandemic, in 2020, there was a major need to find modern and analytical ways to drive results. New innovations were implemented, many tests were run, and this professional’s role gained strong visibility in the market.
A data scientist is the professional capable of extracting useful information and generating insights from data, using advanced knowledge of programming, statistics, and data analysis to support data collection, analysis, and interpretation. In an organization, generally speaking, a basic data team is usually made up of engineers, analysts, and data scientists and is responsible for managing and analyzing company data with the goal of generating actionable insights, determining how data will be collected, stored, and analyzed, in addition to identifying which implementations are necessary so the company culture supports data usage.
Within this team, a data scientist fulfills a very important function, as these professionals have both technical and business knowledge, are responsible for understanding the key issues within the company that need to be solved, and also know how to find the data needed to locate, manage, and analyze large volumes of structured and unstructured data.
Other important responsibilities include:
Data collection and processing;
Exploratory data analysis, including visualization to identify patterns and trends;
Statistical modeling;
A company’s data maturity reports;
Hypothesis development and testing;
Development;
Model testing and optimization;
Model deployment.
When we think about a business context, because they are in direct contact with this information, they are also responsible for identifying organizational improvement opportunities to support the company’s data strategy and generate ROI (return on investment), suggesting improvements such as boosting data literacy and proposing actions that improve communication across teams, while also collaborating with one or more teams (for example: marketing and product) to support efficient data use across the organization.
Some important skills that a good data scientist should have are:
Programming: Currently, data scientists need deep knowledge and must know how to work with R and SQL, in addition to programming languages such as Python.
Statistics: Since they will be in direct contact with data, this professional must have strong knowledge of statistics, including theory, hypothesis testing, regression analysis, and data visualization.
Strong communication: This professional is more capable than anyone of identifying improvement points that need to be implemented within the organization, so good communication is essential, especially when creating reports and documentation.
The role of a data scientist can be highly strategic, as they play a very important function working with business data, in how it is used, with the aim of increasing ROI, in addition to helping implement a more data-driven culture within the company.
The data field is quite broad, and in some industries this professional’s functions may vary and have a greater or lesser decision-making role, but their contribution is always essential. At the start of your data-driven journey, they help implement processes and techniques for data processing. As your company evolves, they can extract more insights and generate value in a more agile and practical way from data.
If your company is not yet ready to bring one of these professionals onto the team, there are very simple and effective ways to outsource the hiring of data teams by contracting hourly packages from more experienced data companies, which provide technology, know-how, and a complete team to help your company take its first steps on its data-driven journey, ensuring access not only to data scientists, but also to experienced analysts and engineers.
When it comes to extracting value from your data, relying only on a data scientist is often not enough to meet all your company’s demands, and at that moment being able to count on a strategic partner that can provide a complete data team is also important.
Erathos also offers a data squad as a service, providing a complete team of specialists aligned with the market’s top practices and latest developments to deliver solutions that are most aligned with your business.