The Types of Data Science Roles Explained

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

Start for Free
Aleksandra Yosifova 23 May 2024 6 min read

Working with data is a meta skill; there’s not only one specific skill set required to succeed in this line of work. Although this creates many opportunities, it can become overwhelming when looking for a job.

Companies recruit for various data science roles, and each of these positions requires different competencies. So, our aim is to describe who does what and how data professionals with different job titles contribute to a business.

Watch the video below for a brief overview of the topic and read on to learn more.

Types of Data Science Roles: Table of Contents

  1. Data Strategist
  2. Data Architect
  3. Data Engineer
  4. Data Analyst
  5. Business Intelligence Analyst
  6. Data Scientist
  7. ML Ops Engineer
  8. Data Product Manager
  9. Types of Data Science Roles: Next Steps

Data Strategist

Ideally, before a company collects any data, it hires a data strategist—a senior professional who understands how data can create value for businesses.

According to the famous data strategist Bernard Marr, there are four main ways how companies in different fields can use data science:

  • They can make data-driven decisions.
  • Data can help create smarter products and services.
  • Companies can use data to improve business processes.
  • They could create a new revenue stream via data monetization.

Companies often outsource such data roles. They hire external consultants to devise a plan that aligns with the organizational strategy.

Once a firm has a data strategy in place, it is time to ensure data availability. This is when a data architect comes into play.

Data Architect

A data architect (or data modeler) plans out high-level database structures. This involves the planning, organization, and management of information within a firm, ensuring its accuracy and accessibility. In addition, they must assess the needs of business stakeholders and optimize schemas to address them.

Such data roles are of crucial importance. Without proper data architecture, key business questions may remain answered due to the lack of coherence between different tables in the database.

A data architect is a senior professional and often a consultant. To become one, you’d need a solid resume and rigorous preparation for the interview process.

Data Engineer

The role of data engineers and data architects often overlaps—especially in smaller businesses. But there are key differences.

Data engineers build the infrastructure, organize tables, and set up the data to match the use cases defined by the architect. What’s more, they handle the so-called ETL process, which stands for Extract, Transform, and Load. This involves retrieving data, processing it in a usable format, and moving it to a repository (the firm’s database). Simply put, they pipe data into tables correctly.

Typically, they receive many ad-hoc ETL-related tasks throughout their work but rarely interact with business stakeholders directly. This is one of the best-paid data scientist roles, and for good reason. You need a plethora of skills to work in this position, including software engineering.

Okay, let’s recap.

As you can see, the jobs in data science are interlinked and complement each other, but each position has slightly different requirements. First come data strategists who define how data can serve business goals. Next, the architect plans the database schemas necessary to achieve the objectives. Lastly, the engineers build the infrastructure and pipe the data into tables.

An infographic showing the link between the data science roles (strategist, architect, engineer, data analyst, and BI analyst) and how combined they create a unified business and data strategy and lead to business insights.

When it comes to deriving insights, we can distinguish between three data roles—data analyst, BI analyst, and data scientist. Although there’s a big overlap, these are separate positions.

The 365 Introduction to Data Science Course provides a detailed yet simple explanation of these roles. Here’s a brief overview of the main differences between data science, BI, and analytics job titles.

Data Analyst

Data analysts explore, clean, analyze, visualize, and present information, providing valuable insights for the business. They typically use SQL to access the database.

Next, they leverage an object-oriented programming language like Python or R to clean and analyze data and rely on visualization tools, such as Power BI or Tableau, to present the findings.

Business Intelligence Analyst

Data analyst’s and BI analyst’s duties overlap to a certain extent, but the latter has more of a reporting role. Their main focus is on building meaningful reports and dashboards and updating them frequently. More importantly, they have to satisfy stakeholders' informational needs at different levels of the organization.

Data Scientist

A data scientist has the skills of a data analyst but can leverage machine and deep learning to create models and make predictions based on past data.

We can distinguish three main types of data scientists:

  • Traditional data scientists
  • Research scientists
  • Applied scientists

A traditional data scientist does all sorts of tasks, including data exploration, advanced statistical modeling, experimentation via A/B testing, and building and tuning machine learning models.

Research scientists primarily work on developing new machine learning models for large companies.

Applied scientists—frequently hired in big tech and larger companies—boast one of the highest-paid jobs in data science. These specialists combine data science and software engineering skills to productionize models.

More prominent companies prefer this combined skillset because it allows one person to oversee the entire ML implementation process—from the model building until productionization—which leads to quicker results. An applied scientist can work with data, model it for machine learning, select the correct algorithm, train the model, fine-tune hyperparameters, and then put the model in production.

As you can see, there’s a significant overlap between data scientists, data analysts, and BI analysts. The image below is a simplified illustration of the similarities and differences between these data science roles.

The link between the roles of a data scientist, data analyst, and BI analyst. A BI analyst analyses past data and reports business insights based on it. A data analyst conducts statistical modeling to make sense of data. A data scientist uses predictive analytics and machine learning, focusing on the future.

ML Ops Engineer

Companies that don’t have applied scientists hire ML Ops engineers. They are responsible for putting the ML models prepared by traditional data scientists into production.

In many instances, ML Ops engineers are former data scientists who have developed an engineering skillset. Their main responsibilities are to put the ML model in production and fix it if something breaks.

Data Product Manager

The last role we discuss in this article is that of а product manager. The person in this position is accountable for the success of a data product. They consider the bigger picture, identifying what product needs to be created, when to build it, and what resources are necessary.

A significant focus of such data science roles is data availability—determining whether to collect data internally or find ways to acquire it externally. Ultimately, product managers strategize the most effective ways to execute the production process.

Here is a summary of the roles discussd in this article: 

Role

Primary Focus

Key Responsibilities

Interaction with Stakeholders

Skillset Required

Data Strategist

Defining how data can serve business goals

Making data-driven decisions

Creating smarter products/services

Improving business processes

Data monetization

High

Strategic planning, business acumen

Data Architect

Planning database schemas to achieve business objectives

Designing high-level database structures

Ensuring data accuracy and accessibility

Optimizing schemas for business needs

Moderate

Information management, database design

Data Engineer

Building infrastructure and managing data flow

Building and organizing data infrastructure

Handling ETL processes

Low

Software engineering, data management skills

Data Analyst

Analyzing and visualizing data for insights

Exploring, cleaning, analyzing, and visualizing data

Presenting information

Moderate

SQL, Python/R, Excel, data visualization tools (e.g., Power BI, Tableau)

Business Intelligence Analyst

Reporting and dashboarding for business insights

Building and updating reports and dashboards

Satisfying informational needs at different organizational levels

High

Reporting tools, data visualization, understanding of business needs

Data Scientist

Predictive analytics and machine learning

Advanced statistical modeling

Building and tuning machine learning models

Experimentation via A/B testing

Moderate

Advanced analytics, machine learning, programming

ML Ops Engineer

Operationalizing machine learning models

Putting ML models into production

Managing model maintenance and updates

Moderate

Engineering skillset, understanding of machine learning operations

Data Product Manager

Overseeing the success of a data product

Identifying product needs

Strategizing product creation and resource allocation

Ensuring data availability

High

Product management, strategic vision, data understanding

Types of Data Science Roles: Next Steps

We hope this helps you navigate the multifaceted world of data science. You can choose between a variety of data science roles; find your niche and start preparing for your dream job.

You can begin with one of our career tracks – they cover the fundamentals and build up your skillset through practical examples and exercises. Sign up now and begin your path to a successful career as a Data Scientist, Data Analyst, or Business Analyst.

 

FAQs

What are the roles of data science?
Data science encompasses various roles, each contributing to data collection, management, analysis, and interpretation of data. These roles include (among others) data strategists, who define how data can serve business goals; data architects, who design and manage database structures; data engineers, who build and maintain data infrastructure; data analysts, who analyze and visualize data; business intelligence analysts, who focus on reporting and dashboards; data scientists, who use predictive analytics and machine learning; ML ops engineers, who operationalize ML models; and data product managers, who oversee the success of data products.
 
To gain a comprehensive understanding of these roles, explore the Data Scientist Career Track on our 365 Data Science platform.

 

What are the different careers in data science?
Careers in data science offer a broad spectrum of opportunities—ranging from technical roles to strategic positions, each tailored to different skill sets and professional interests. On the technical side, we have data engineers and data scientists. Data engineers are responsible for building and maintaining the robust infrastructure required for optimal data flow and storage, ensuring data is accessible and secure for analysis. They handle large-scale processing systems and work on the backend aspects of data handling. Meanwhile, data scientists explore this data to conduct complex analyses—applying statistical and machine learning techniques to uncover patterns that predict future trends and behaviors, thereby informing business strategies.
 
Data strategists play a crucial role on the strategic front. They bridge the gap between the data and its business application, crafting strategies that leverage data to drive business growth and operational efficiency. These professionals must possess a deep understanding of both the data itself and the broader business context—enabling them to guide data-driven decision-making processes at the highest levels of an organization.
 
On the business side, BI analysts focus more on reporting and visualization, creating dashboards and reports that provide actionable insights to various company stakeholders. They transform complex data findings into understandable visual presentations, making it easier for non-technical audiences to make informed decisions.
 
At 365 Data Science, we provide specialized courses and resources for these careers and more. Check out our structured career tracks and real data projects to build a standout portfolio.

 

What is the highest role in data science?
The highest role in data science can vary depending on the organization's structure and needs. In many companies, senior roles like Chief Data Officer (CDO) or Vice President of Data Science represent the pinnacle, overseeing the strategic use of data across the organization. For hands-on technical careers, roles such as Lead Data Scientist or Director of Data Science are typically considered top positions. To aim for these high-level roles, our data science courses provide the necessary knowledge and skills through practical examples, exercises, and projects.

 

Which two are common roles in the data science field?
Data analysts and data scientists are two of the most prevalent roles in data science.
 
Data analysts collect, process, and perform statistical analyses on large datasets. They employ tools like SQL for data extraction, Excel or Python for data manipulation, and Tableau or Power BI for data visualization and reporting. Data analysts play a fundamental role in interpreting and translating data into actionable insights, which help organizations make data-driven decisions. On the other hand, data scientists are often seen as more advanced roles. They employ advanced algorithms and machine learning techniques to predict future trends, behaviors, and outcomes.
 
Data scientists use such programming languages as Python or R, and machine learning platforms like TensorFlow or PyTorch. They also design and implement models that allow companies to understand their data on a deeper level, revealing patterns and relationships that were previously unseen.
 
Interested in these roles? Our 365 Data Science program offers in-depth training and resources to help you get started and advance in these careers.

 

Aleksandra Yosifova

Blog author at 365 Data Science

Aleksandra is a Copywriter and Editor at 365 Data Science. She holds a bachelor’s degree in Psychology and is currently pursuing a Master’s in Cognitive Science. Thanks to her background in both research and writing, she learned how to deliver complex ideas in simple terms. She believes that knowledge empowers people and science should be accessible to all. Her passion for science communication brought her to 365 Data Science.

Top