Job roles in data science and artificial intelligence

Is data the oil of the 21st century? Is being a data scientist really the most attractive job of the century? Does it pay well? Are we going to lose our jobs to AI?

All of those questions came out of the boom in data and artificial intelligence over the last decade. There’s no definitive answer to any of them, but one thing is certain: the ability to collect, analyse and use data effectively became central, and that created growing demand for specialised profiles.

This is a tour of the field’s most relevant roles: what each does, what skills it asks for and how it contributes to the wider picture.

Salary figures are 2024 reference points and vary widely by industry, company size and type of contract.

Data scientist

One of the best-known roles. Analyses and interprets large volumes of data using statistical and machine learning techniques to extract knowledge and support strategic decisions.

Responsibilities: developing predictive models and machine learning algorithms; running exploratory analysis; communicating findings and recommendations; working with other areas to implement data-driven solutions.

Key skills: programming in Python, R or SQL; statistics and mathematics; visualisation tools such as Power BI or Tableau.

Reference salary: United States, 95,000–120,000 a year. Latin America, 30,000–60,000.

Data analyst

Focuses on collecting, processing and analysing data to produce reports and visualisations that support business decisions.

Responsibilities: gathering and cleaning data from various sources; building dashboards and reports to monitor key metrics; identifying trends and patterns; working closely with business areas to understand what they need.

Key skills: advanced Excel and SQL; business intelligence tools; the ability to interpret data and present findings clearly.

Reference salary: United States, 60,000–80,000. Latin America, 20,000–40,000.

“Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondergaard

Data engineer

Designs, builds and maintains the infrastructure needed to store and process large volumes of information.

Responsibilities: designing and building data pipelines; ensuring integrity and quality; optimising databases and storage systems; implementing cloud solutions.

Key skills: programming in Python, Java or Scala; SQL and NoSQL databases; big data platforms such as Hadoop and Spark.

Reference salary: United States, 100,000–130,000. Latin America, 35,000–70,000.

Data architect

Responsible for an organisation’s overall data structure and management strategy. The goal is for data to be handled efficiently and securely.

Responsibilities: defining data architecture and governance policies; ensuring scalability and security; working with engineering and data science on implementation; evaluating and selecting technologies.

Key skills: thorough database design; IT project management; the ability to write and uphold governance policy.

Reference salary: United States, 120,000–150,000. Latin America, 40,000–80,000.

Business analyst specialising in data

Uses quantitative and qualitative analysis to understand business needs and translate them into data-driven solutions.

Responsibilities: identifying problems and opportunities through analysis; working with technical teams on solutions; measuring the impact of initiatives; communicating findings to management.

Key skills: analysis and problem solving; business analysis techniques; the ability to communicate complex ideas effectively.

Reference salary: United States, 70,000–90,000. Latin America, 25,000–50,000.

Machine learning engineer

Specialises in designing and developing machine learning models that can learn and predict from data.

Responsibilities: developing and training models; taking them to production; optimising algorithms; working with data science to turn business problems into technical solutions.

Key skills: Python, Java or C++; frameworks such as TensorFlow, PyTorch or Scikit-learn; a deep understanding of algorithms and statistics.

Reference salary: United States, 110,000–140,000. Latin America, 35,000–70,000.

“Artificial intelligence isn’t the future, it’s the present.”

Fei-Fei Li

AI engineer

Focuses on developing systems capable of simulating cognitive processes: learning, reasoning, decision-making.

Responsibilities: designing and developing AI algorithms; implementing them in practical applications; contributing to research and development; integrating AI solutions alongside product teams.

Key skills: Python, R or Java; techniques such as neural networks, natural language processing and computer vision; frameworks such as TensorFlow and Keras.

Reference salary: United States, 120,000–150,000. Latin America, 40,000–80,000.

AI researcher

Dedicated to researching and developing new techniques and algorithms.

Responsibilities: researching new techniques; publishing and presenting findings at conferences; collaborating with universities and research centres; testing and validating new approaches.

Key skills: solid mathematics and statistics; advanced knowledge of algorithms and theory; the ability to research independently and in a team; academic publication.

Reference salary: United States, 130,000–160,000. Latin America, 45,000–90,000.

Closing

The field covers a range of roles, each with its own set of skills and responsibilities. From scientists and analysts to engineers and architects, and now AI-specialised profiles too, each plays a part in turning data into something useful.

If you’re considering a career here, there’s more than one viable path, and it’s worth choosing the one that matches what you actually want to do, rather than the one with the most striking headline.