What does a data scientist do?
Data scientists analyze and model data to drive business insights. Learn about their responsibilities, technical skills, and how they differ from data engineers and analytics engineers.
What does a data scientist do?
Looking for the right professional to join your data team? In this article, we'll explain what a data scientist does, what their role is in your company, and how they differ from a data engineer or data analyst. In 2017, The Economist published an article on the rise of a new commodity that would be even more valuable to the global economy than oil: data. Today, this prediction is a consolidated reality for companies; the rules of the corporate game have changed, and the market has adapted.
Data is no longer part of a distant and remote future. While it is possible to do business without leveraging data strategically, companies that don't do it—or don't even think about doing it—are leaving money on the table. For this reason, you need professionals on your team who can handle this demand efficiently, and a Data Scientist can act in a highly strategic way.
The role of data scientists in data teams
A data scientist is a professional capable of extracting useful information and generating insights from data, using advanced knowledge of programming, statistics, and data analysis to assist in its collection, analysis, and interpretation. Generally, in an organization, a typical data team consists of data engineers, data analysts, and data scientists. They are responsible for managing and analyzing company data to generate actionable insights, determining how it will be collected, stored, and analyzed.
Within this team, a data scientist plays a crucial role. They are professionals with both technical and business acumen, responsible for understanding the key business questions that need solving. They also know how to find the necessary data to locate, manage, and analyze large volumes of structured and unstructured data.
Other key responsibilities include:
Data collection and cleaning
Exploratory data analysis (EDA), including visualization to identify patterns and trends
Statistical modeling
Company data maturity reporting
Hypothesis generation and testing
Model development, testing, and optimization
Model deployment
In a business context, because they are in direct contact with this information, data scientists are also responsible for identifying organizational improvement areas for the company's data strategy and driving ROI. They suggest initiatives like boosting data literacy and improving communication across departments, collaborating with one or more teams (such as marketing and product) to help the organization use data efficiently.
Some key skills a great data scientist should have are:
Programming: deep knowledge of R and SQL, along with programming languages like Python.
Statistics: strong background in statistical theory, hypothesis testing, regression analysis, and data visualization.
Great communication: the ability to point out improvement areas that need to be implemented within the organization, which requires strong communication skills, especially when creating reports and documentation.
Data Scientist vs. Data Engineer vs. Data Analyst
It is common to confuse these three roles, and the line between them varies from company to company, but there is a typical division of responsibilities:
Data engineers build and maintain the infrastructure: ingestion pipelines, storage architecture, and modeling tables for consumption. They ensure data gets to the right place, in the right format, reliably. Without this foundational work, neither of the other two roles has clean data to work with.
Data analysts work on top of already modeled data, answering recurring business questions through dashboards, reports, and operational metrics. The focus is on explaining "what happened" and "what is happening now" with clarity for decision-makers.
Data scientists go beyond descriptive questions to predictive and prescriptive ones: building statistical and machine learning models, testing hypotheses, and developing custom solutions that don't come out of the box in any dashboard. This is the closest role to applied research on the data team.
In practice, smaller companies often have one person handling two or even all three roles. It's only in larger teams, with sufficient data volume and maturity, that this division becomes clear in separate roles. If you are building this type of team from scratch, it's worth reading Engenharia de Dados para Startups, which details how to prioritize each piece (ingestion, transformation, modeling, analysis) according to the company's stage before deciding which role to hire first.
When to hire a data scientist
The data landscape is vast, and in some industries, the roles of this professional may vary, playing a more or less decision-making role, but their presence is always essential. Early in a company's data-driven journey, this professional helps implement processes and techniques for data processing. As the company evolves, they can extract more insights and generate business value more agilely and practically from data.
If your company is not yet ready to bring on a dedicated data scientist, it is worth understanding where your data sources are scattered across the organization and how to centralize them, even without a formal data team. This is the scenario we detail in Centralização de dados sem time dedicado.
Frequently asked questions about the data scientist role
What is the difference between a data scientist and a data engineer? A data engineer builds and maintains the infrastructure that moves data from source to warehouse in a reliable, modeled way. A data scientist uses this available data to build statistical models and answer predictive questions, rather than just descriptive ones.
Do I need a data scientist before a data engineer? Generally, no. Without a centralized, reliable data foundation maintained by a data engineer (or a managed ingestion tool), a data scientist's work is limited, as they will spend most of their time dealing with messy, scattered data instead of modeling.
Which programming languages does a data scientist need to know? Python and SQL are practically mandatory today. R still frequently appears in more statistical and academic contexts.
Does a small startup need a dedicated data scientist? In most cases, not from the start. In the early stages, the return is usually higher when prioritizing reliable data ingestion and basic descriptive analysis. A dedicated data scientist tends to make more sense when there is already sufficient data volume and business questions shift from "what happened" to "what will happen."
Conclusion
The role of a data scientist can be highly strategic, leveraging business data to increase ROI and help implement a more data-driven culture within the company. However, this work depends on a solid foundation of centralized data, which is usually the first step before even considering hiring for this role.
Want to understand how to build this foundation before assembling a full data team? Book a call with us or create your free account on Erathos and see how to centralize your data without needing a dedicated engineering team from day one.
What does a data scientist do?
Looking for the right professional to join your data team? In this article, we'll explain what a data scientist does, what their role is in your company, and how they differ from a data engineer or data analyst. In 2017, The Economist published an article on the rise of a new commodity that would be even more valuable to the global economy than oil: data. Today, this prediction is a consolidated reality for companies; the rules of the corporate game have changed, and the market has adapted.
Data is no longer part of a distant and remote future. While it is possible to do business without leveraging data strategically, companies that don't do it—or don't even think about doing it—are leaving money on the table. For this reason, you need professionals on your team who can handle this demand efficiently, and a Data Scientist can act in a highly strategic way.
The role of data scientists in data teams
A data scientist is a professional capable of extracting useful information and generating insights from data, using advanced knowledge of programming, statistics, and data analysis to assist in its collection, analysis, and interpretation. Generally, in an organization, a typical data team consists of data engineers, data analysts, and data scientists. They are responsible for managing and analyzing company data to generate actionable insights, determining how it will be collected, stored, and analyzed.
Within this team, a data scientist plays a crucial role. They are professionals with both technical and business acumen, responsible for understanding the key business questions that need solving. They also know how to find the necessary data to locate, manage, and analyze large volumes of structured and unstructured data.
Other key responsibilities include:
Data collection and cleaning
Exploratory data analysis (EDA), including visualization to identify patterns and trends
Statistical modeling
Company data maturity reporting
Hypothesis generation and testing
Model development, testing, and optimization
Model deployment
In a business context, because they are in direct contact with this information, data scientists are also responsible for identifying organizational improvement areas for the company's data strategy and driving ROI. They suggest initiatives like boosting data literacy and improving communication across departments, collaborating with one or more teams (such as marketing and product) to help the organization use data efficiently.
Some key skills a great data scientist should have are:
Programming: deep knowledge of R and SQL, along with programming languages like Python.
Statistics: strong background in statistical theory, hypothesis testing, regression analysis, and data visualization.
Great communication: the ability to point out improvement areas that need to be implemented within the organization, which requires strong communication skills, especially when creating reports and documentation.
Data Scientist vs. Data Engineer vs. Data Analyst
It is common to confuse these three roles, and the line between them varies from company to company, but there is a typical division of responsibilities:
Data engineers build and maintain the infrastructure: ingestion pipelines, storage architecture, and modeling tables for consumption. They ensure data gets to the right place, in the right format, reliably. Without this foundational work, neither of the other two roles has clean data to work with.
Data analysts work on top of already modeled data, answering recurring business questions through dashboards, reports, and operational metrics. The focus is on explaining "what happened" and "what is happening now" with clarity for decision-makers.
Data scientists go beyond descriptive questions to predictive and prescriptive ones: building statistical and machine learning models, testing hypotheses, and developing custom solutions that don't come out of the box in any dashboard. This is the closest role to applied research on the data team.
In practice, smaller companies often have one person handling two or even all three roles. It's only in larger teams, with sufficient data volume and maturity, that this division becomes clear in separate roles. If you are building this type of team from scratch, it's worth reading Engenharia de Dados para Startups, which details how to prioritize each piece (ingestion, transformation, modeling, analysis) according to the company's stage before deciding which role to hire first.
When to hire a data scientist
The data landscape is vast, and in some industries, the roles of this professional may vary, playing a more or less decision-making role, but their presence is always essential. Early in a company's data-driven journey, this professional helps implement processes and techniques for data processing. As the company evolves, they can extract more insights and generate business value more agilely and practically from data.
If your company is not yet ready to bring on a dedicated data scientist, it is worth understanding where your data sources are scattered across the organization and how to centralize them, even without a formal data team. This is the scenario we detail in Centralização de dados sem time dedicado.
Frequently asked questions about the data scientist role
What is the difference between a data scientist and a data engineer? A data engineer builds and maintains the infrastructure that moves data from source to warehouse in a reliable, modeled way. A data scientist uses this available data to build statistical models and answer predictive questions, rather than just descriptive ones.
Do I need a data scientist before a data engineer? Generally, no. Without a centralized, reliable data foundation maintained by a data engineer (or a managed ingestion tool), a data scientist's work is limited, as they will spend most of their time dealing with messy, scattered data instead of modeling.
Which programming languages does a data scientist need to know? Python and SQL are practically mandatory today. R still frequently appears in more statistical and academic contexts.
Does a small startup need a dedicated data scientist? In most cases, not from the start. In the early stages, the return is usually higher when prioritizing reliable data ingestion and basic descriptive analysis. A dedicated data scientist tends to make more sense when there is already sufficient data volume and business questions shift from "what happened" to "what will happen."
Conclusion
The role of a data scientist can be highly strategic, leveraging business data to increase ROI and help implement a more data-driven culture within the company. However, this work depends on a solid foundation of centralized data, which is usually the first step before even considering hiring for this role.
Want to understand how to build this foundation before assembling a full data team? Book a call with us or create your free account on Erathos and see how to centralize your data without needing a dedicated engineering team from day one.