Top 5 Data Science Roles in Europe

The field of data science has been ever-growing. The vast domains of data science offer a variety of roles for skilled folks to be experts. With every passing day, the applications of data science are increasing and so is the demand for data scientists.

It can be daunting for someone who wants to make a career in data science or is already planning to upskill in data science to get clarity on different job roles and responsibilities under the umbrella. In recent times, the demand for data science has increased significantly in the UK and the rest of Europe. According to sources, the demand across data science roles has tripled in the past five years, rising 231%. It’s implied that the demand for data science skills is bound to increase.

You can check out various Data Science courses by McCombs School of Business at the University of Texas at Austin and MIT, offered in collaboration with Great Learning, where you get to learn from award-winning professors and industry experts through online mentorship sessions and 24×7 dedicated program support. These courses are also curated in Data science for beginners.

Here, we have tried listing down the top roles in data science with their responsibilities, so that you have a better understanding of the overlapping and exclusive responsibilities in each role.

Let’s go through each one of them and understand the skills required to excel in the particular role.

Data Scientist:

What is a Data Scientist?

The core job of a Data Scientist is to solve business problems with solutions derived from data analysis and data processing. They are required to organize scattered and unstructured data and derive actionable insights out of them.

What Skills are Required for a Data Scientist?

Expertise needed in R, SAS, SQL, Hive, Pig, Spark, Python, etc.
Should have the expertise or sound knowledge of quantitative subjects, analytical tools, and big data technologies.

Data Scientist roles and responsibilities:

Identify data sources as per requirements of the business
Source missing data, collect data from primary and secondary sources
Build algorithms and predictive models for organizing data, visualizing data, and deriving insights
Identify trends and patterns in data relevant to the business needs
Create, implement, and maintain databases
Create solutions for the whole data processing cycle starting from data collection to data presentation

Data Analyst:

What is a Data Analyst?

As the title itself suggests, the core responsibility of that a Data Scientist is to work with data to help the organization make data-driven business decisions. Data Analysts are the foundations of any analytics team and they tend to be more a generalist who is versed in all sorts of mathematical and statistical analysis. Being a generalist, a data analyst is generally required to wear several hats to serve business requirements.

What Skills are Required for a Data Analyst?

Expected to have a working knowledge of R, SAS, Python, HTML, C++, SQL, and Javascript.
Have problem-solving skills, and need familiarity with data retrieval, data warehousing, and data visualization using several technical tools.

Data Analyst roles and responsibilities:

Analyze data using several statistical and mathematical models
Develop algorithms and databases, implement and maintain them
Filter and clean data, make data more readable
Work with cross-functional teams for their requirements
Perform queries on databases and look into business key metrics
Perform AB testing for data models
Convert raw data into a presentable format for stakeholders

Data Engineer:

What is a Data Engineer?

The primary responsibility of a Data Engineer is to develop, implement and maintain data systems. They design data pipelines and prepare and test ecosystems for data scientists to run their algorithms on. They make sure that the raw data collected from primary and secondary sources are ready to be processed and analyzed.

What skills are required for a Data Engineer?

Have expertise in programs and frameworks like Apache Hadoop and Apache Spark, C++, AWS, Azure, etc
Have an understanding of database systems, warehousing solutions, ETL tools, and programming languages like Python, and Java, and have sound knowledge of algorithms and data structures.

Data Engineer roles and responsibilities:

Analyze raw data, develop, maintain, evaluate and test data solutions within organizations.
Support data analysts by making data ready to work on and suitable for running algorithms and frameworks.
Identify ways to improve data quality and efficiency
Use data sets to address business problems
Find trends and patterns using data
Work with teams cross-functionally and update stakeholders

Data Architect:

What is a Data Architect?

The data architect title is frequently confused with that of a Data engineer. While there might be some overlapping responsibilities, the major difference in both the roles lies in their primary responsibility.

What skills are required for a Data Architect?

Hands-on with applied mathematics and statistics, accompanied by basic programming languages
Expertise in data visualization, data migration, data processing, data modeling, database management, and data mining.

Data Architect roles and responsibilities:

Develop, design, and implement the overall data strategy for the organization
Convert the business requirements into technical specifications
Define data flows and reference architectures
Plan and manage end-to-end data architecture
Maintain database systems
Define data architecture standards, frameworks, and principles
Collaborate with multiple stakeholders for several business requirements

Machine Learning Engineer:

What is a Machine Learning Engineer?

The core responsibility of a Machine Learning Engineer is to create programs and algorithms that make it possible for the machines to take actions without being directed. Machine Learning engineers make it possible for computers to learn automatically and keep improvising from experience, without being manually programmed.

What skills are required for a Machine Learning Engineer?

Have an understanding of concepts of applied mathematics, computer science, software engineering, data analysis, and feature engineering.
Have expertise in ML algorithms and cross-validation

Machine learning Engineer roles and responsibilities:

Develop machine learning systems and algorithms
Manage the infrastructure and data pipelines to bring the code to production
Analyze large data sets to derive useful information
Collaborate with data engineers to build data and model pipelines
Maintain and extend existing frameworks and libraries
Research on Machine learning algorithms and continually test machine learning systems
Collaborate with stakeholders to identify business requirements and define the needed solution’s scope

While there are major roles that we discussed in this article, a variety of technical roles also depend on the kind of industry and business you work for. Nevertheless, the markets for data science and related fields are on a boom and it’s a golden opportunity for someone to build a career in the same.

Wish to upskill in data science and related fields? Check the programs below offered by Great learning in collaboration with top-notch universities in data science, AI and Machine Learning.