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Everything You Need to Know About Data Science Jobs & Careers

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Technological advancements have placed data at the forefront of every business, it’s incredible ability to illustrate and highlight key findings makes it invaluable for business analysis. With a postgraduate degree of data science in Australia, you gain the skills to gather data and view the world with a more nuanced lens, making you a very important asset to business leaders and decision-makers. LinkedIn released a report in which a career in data science topped the Emerging Jobs list for three years running with an annual growth rate of 37%, making it one of the top jobs of the 21st century. Appealing to individuals experienced or interested in IT, mathematics and problem solving, a data scientist requires a critical and analytical mindset. 

Data science is a broad field that is constantly evolving alongside technology, it has grown to include various jobs and career pathways. Data scientists are continuously working to expand the capabilities of data extraction and machine learning, they are asking new questions on how far we can push technological advancements. Conversely, data analysts find meaningful insights and trends within datasets - they look to find answers to the questions raised by data scientists. Data science roles can overlap and change, however, there are specific categories within this field and it is useful to know which role will best compliment your skills and interests. 

Discover how to become a data scientist >

Types of data scientist jobs & pathways 

Data Scientist

Expected salary in Australia: $63K - $141K 

Data scientists explore and forge the path in this multidisciplinary field, they seek new ways to obtain data through computer science, predictive analysis, statistics and machine learning. Data scientists can specialise in a specific field or keep their skills broad and manage intersecting projects. Data scientists are constantly making technological breakthroughs and establishing new areas of study within the industry. This position requires problem-solving skills and creativity to lead data into the future. 

Key responsibilities:

  • Identify data sources and automate collection processes. 
  • Build predictive models and machine-learning algorithms. 
  • Propose solutions and strategies to business challenges.
  • Collaborate with the engineering and product development teams.

Data Analyst

Expected salary in Australia: $50k – $103k

Leading IT publication, Techopedia, describes a data analyst as someone who “works with data to provide insights”, they identify key figures and present relevant information to enhance business decisions. They are trained to locate strengths and weaknesses, through the collecting of information, identifying patterns and trends from data pools. 

Learn more about the art of storytelling with data visualisation >

Forbes outlines that data analysts require good technical skills and are not only able to identify patterns within data but also transform their findings into a data strategy and actionable insights that can be understood by stakeholders. Being an excellent communicator and having the capability to present findings is a crucial element of the role. Data analysts take large-scale information and transform it into important and concise findings.

Key responsibilities:

  • Collecting and interpreting data about relevant topics of importance to businesses.
  • Analysing results and reporting the insights back to the business.
  • Maintaining databases.


Expected salary in Australia: $58k - $120k

For people who love working with numbers, statisticians process and analyse data through mathematical means; collecting, managing and interpreting numerical sets. Statisticians develop useful business predictions. From financial and operational forecasts to patterns in consumer behaviour, they extract projections from data trends while having a strong understanding of programming languages such as R or Python. Their unique and in-demand skills are required across different industries. Seek highlights that statisticians work across the public and private sector with data science jobs in government departments, financial institutions and technology companies. Statisticians can also develop specialisations, for example, criminal statisticians work to understand how people are engaging with the justice system, crime and government. 

Key responsibilities

  • Collect, analyse and interpret data to produce valuable statistics that highlight trends.
  • Ensure that the method of data collection and the analysis methodology is valid, efficient and accurate.
  • Making sense of data science with Python and R programming language.
  • Record, present and represent data graphically, reporting on key findings.

Data Engineer

Expected salary in Australia: $61k – $135k 

Building the infrastructure to support all of the collected data, data engineers build informational systems to organise and store data, ensuring that everything is accurate. Data engineers work alongside analysts to build software solutions that will enhance the collection of such large data pools. This role is great for individuals who love programming and problem solving, requiring someone extremely detail-driven to ensure that complex systems are working simultaneously, delivering the most efficient methods of data collection and data analysis. 

Key responsibilities

  • Create and maintain efficient data pipeline architecture.
  • Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability.
  • Build the data infrastructure required for extraction, transformation and loading of data from a wide range of sources.

Machine Learning Engineer

Expected salary in Australia: $50k - $136k

Machine learning engineers (also called artificial intelligence engineers) work to create detailed systems and AI (artificial intelligence) which collect large complex data sets. With an extremely nuanced understanding of programming and machine learning, this role will create tools for data extraction. A machine learning engineer requires a strong background in engineering or computer science as they are not only understanding data but building the programs which house it.

Discover how automation is improving data science techniques >

Key responsibilities:

  • Designing and developing machine learning models and deep learning systems.
  • Running machine learning tests and experiments.
  • Implementing appropriate machine learning algorithms.

Whilst these are a few key career paths within data science, the possibilities are limitless. The industry is rapidly growing, and different roles are constantly emerging. Students undertaking data science come from a diverse range of backgrounds, from business and economics to science, IT and engineering – data science and data analytics are a multifaceted field. Studying an online Graduate Diploma of Data Science can keep you relevant in this fast-moving world of data.

Who needs data scientists?

To pursue a career path in data science, individuals require specific technical and mathematical skills. However, there is a broad range of industries offering job opportunities for which you are in demand, allowing you to pursue personal interest areas. Data science job postings increased by 34% in 2018 and have not shown any signs of slowing down. As a result, data scientists in Australia and around the world have become crucial to business intelligence (BI) teams. Whether you are interested in sport, fashion or finance, data scientists work to pave the way forward. 

Industries to note are:

  • Global financial institutions
  • Government organisations
  • Technology start-ups
  • Health
  • Public administration and safety

These fields rely on data scientists to collect their information and process it to make advancements that will help the entire community. 

Take a look at this infographic which explains the different courses and industries in data science.

Data science teams are now perceived as valued members of companies, a source of truth and direction when it comes to business decisions. They help to bridge the gap between the business and their consumers, taking masses of consumer data and transforming it into information and actionable insights that can ultimately help companies better serve their customers.      

“The cool thing about data is that you can give the same data set to 100 different people and I guarantee everyone will find some new way to use the data or some new information they derive from the data,” says Data Scientist Seun Aremu, who is currently developing a machine learning framework dedicated to structuring asset data

“You have to be comfortable with the unknown when working with data. You have to trust your skills and trust your research because you’re going into the deep end without knowing where the bottom is.”     

With the Graduate Diploma of Data Science, you can gain credibility in your field and recognition of your expertise, employers will know that you have deep and well-rounded knowledge in data science. Moreover, this degree can help you transition into new positions within, upskilling and tapping into different areas of data and analytics could help you broaden your skillset and drive your career forward

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