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Data scientists - driving our future

Demand for data scientists with the right skills, curiosity and talent to create and manage complex data models has skyrocketed. When used effectively, data can transform businesses, improve decision making, accelerate innovation, improve the customer experience and drive operational efficiency.

But it needs the right people to make this happen. Experts who can convert raw unstructured data and who can turn Big Data into meaningful insights, fast, are in demand. Let us help you to discover the opportunities. 

Find my next data scientist job in Australia 

Almost all organisations are looking at how they can better use data to power their growth. But you’ll need our support to find the right one for you. If you’re a data scientist with the skills to match your ambitions, we’ll give you access to the data science roles nobody else has from a complete range of employers. 

Work with us and you’ll get an expert, lifelong career partner who will help assess your options, listen to your feedback and who will secure your next role quickly – being in demand provides you with the opportunities, we can help you to capitalise on them. 

Find your nearest office to get in touch with us, send us your CV or browse our latest available Data Scientist jobs.

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Your Data Scientist job questions, answered

Where can I find Data Scientist jobs in Australia?

Hays regularly advertises new jobs for Data Scientists right around Australia. Click below to check out all our Data Scientist jobs or those in your nearest city:  
 

What does a Data Scientist do? 

Fuelled by a growing amount of data availability and the progression of artificial intelligence, the demand for data science skills is rising exponentially.  A Data Scientist uses available data and develops complex models or Machine Learning algorithms to help businesses solve current and even future problems through data insights. 
 
They combine computer science, mathematics and business acumen to uncover results that are used to make objective business decisions. 

What skills does a Data Scientist need to have? 

The knowledge and skillset of a Data Scientist exceed data analyst skills because they are required to develop complex models to predict market trends and future behaviours. Therefore, the need to be able to develop and write code using a language such as R, Python or SAS is a must have. 
 
Data visualisation is the ability to present data driven insight in a user-friendly format that can be accessed by a variety of business stakeholders. An important asset for any Data Scientist, visual representations make it easier to identify patterns and trends. We recommend that candidates become familiar with interactive software such as Tableau, Pandas, MatplotLib, D3.js to succeed in this field. 
 
Machine learning is continuously evolving, and basic levels of the concept are quickly becoming a standard requirement for Data Science jobs. This is an extension of mathematics as linear algebra is the backbone of machine learning. 
 
A Data Scientist should be able to develop complex financial or operational models. This includes proficiency in algorithms such as regression, scenario analysis, modelling, simulation, clustering, neural networks, decision trees and beyond. 

What job description and responsibilities does a Data Scientist have? 

  • Collect, gather, analyse, and then visualise information to create insights that will drive business solutions 

  • Source additional information through the creation of new data and utilisation of any and all relevant statistical techniques, software packages, program languages and data infrastructure to resolve specific business issues 

  • Use predictive modelling to optimise business goals and outcomes 

  • Collaborate with other departments to develop and implement custom data models and algorithms 

  • Extract insights from data and communicate these effectively using appropriate technologies to stakeholders and other relevant parties 

  • Streamline and automate processes using artificial intelligence and machine learning 

What skills and experience do employers look for in Data Scientists? 

Degrees in computer science and mathematics are the most common among data scientists. Advanced computer science skills are essential as employers are leaning on data scientists to be both problem solvers and developers. In the rapidly developing discipline of machine learning, sophisticated algorithms are created to learn from the data. Applicants with comprehensive computer science skills will be looked at favourably by employers.
 
Mathematics is a significant component in any data science job as concepts within mathematics are critical in identifying patterns and creating algorithms. Calculus, linear algebra, and statistics are three topics that are regularly used in Data Scientist jobs. It is important to understand principles of calculus and how they might impact your modelling. 
 
Candidates who possess a genuine strength in probabilities and statistics will be in an advantageous position. Calculating the likelihood of an event is an intuitive concept that Data Scientists use daily when employing methods such as scenario analysis. In relation to statistics, you should be familiar with tree-based methods and competent in the application of validation techniques.
 
Business acumen relates to the knowledge and understanding of a particular field. Every industry has its own intricacies, and this should be recognised as an opportunity for you to gain a deep understanding of a particular field in order to stand out.  Learning industry intricacies is a gradual process that will be made easier if you can display strength in other fundamental areas such as computer science and mathematics. 
 
The value of communication as a Data Science skill should not be underestimated. These skills are important during the discovery and goal phase but are more prevalent in the presentation of results. Data Scientists are typically required to communicate their results to key stakeholders, including executives.
 
The ability to deliver results in a manner that is easy to understand, yet insightful and compelling, is where the value of a Data Scientist is exhibited. Employers are looking for Data Scientists who can tell them how the analysed data will impact their business.  

 

Data Science is an expanding field that is constantly evolving to meet technological advances and changing demands. The general route to a Data Scientist is to progress from a BI Analyst or Data Analyst Role with academic understanding of Data Science concepts.
 
Applicants with a background in computer science or mathematics can explore entry-level data analytics, business intelligence and data science jobs before progressing to a senior Data Scientist position. 

What qualifications do I need to apply for a Data Scientist job? 

Whilst you can get a Bachelor’s or Master’s degree in Data Science, a common academic path is qualifications in Computer Science, Maths, or Statistics. However proven relevant experience and a commitment to ongoing learning are essential. 

What type of employers hire Data Scientists? 

There are no boundaries when it comes to the types of employers looking to hire data scientists. Finance, information technology and science are recognised as three of the leading industries when it comes to data science vacancies.
 
In what is a real positive for candidates, an increasing number of industries are looking to utilise data science. They include and are not limited to: 
 
  • Finance - Data Scientists are in genuine demand in financial sectors such as banking, trading, and insurance. Employers are utilising data science to manage customer data, automate risk management, detect fraud, conduct algorithmic trading and real-time analysis.   
  • Mining - A large portion of data science jobs in Australia are related to mining, quarrying and extraction of commodities (oil and gas). If you have a strong background in spectroscopy and/or geology, you may like to consider data science jobs in Perth. 
  • Healthcare - Imaging is a key area as computers can learn to interpret images such as MRI’s and X-rays. They can also identify patterns in data and detect tumours.  
  • Marketing - Big data in marketing provides an opportunity to better understand the target audience. Data science can be applied to areas relating to customer engagement, retention, dynamic pricing, profiling, and search engine optimisation. 
  • Transport, Security and Defence - These fields may seem different by nature but share similarities with respect to data science. Facial recognition, fingerprinting, surveillance, and traffic analysis are all areas that provide data science jobs. 
Other fields include engineering, manufacturing, wholesale trade, public administration, meteorology, and agriculture. 

What technologies does a Data Scientist use? 

These can be separated into several categories. The use of these technologies and requisite knowledge will vary based on employer.

They include and are not limited to: 

 

  • Programming Languages - Python, R and SQL 
  • Machine Learning & Artificial Intelligence – BigML and Apache (Spark, Hadoop etc)  
  • Mathematics/Statistics - SAS and MATLAB 
  • Data Visualisation - Tableau, D3.js, Qlikview, Matplotlib,Microsoft BI 

How much do Data Scientists earn? 

Data science is an expanding field with lucrative career opportunities. As a result, the average salary for the industry continues to rise.  
 
Advanced data scientists are in very high demand and can command up to 6-figure salaries. Typical roles in Sydney average around $165,000, but can be as high as $260,000 depending on scope of role and the size of the organisation. 
 
For our latest guide on typical salaries as a Data Scientist, please refer to our  Hays Salary Guide

Are data and analytics professionals in high demand? 

Absolutely. Data is powering the world around us and is now an essential part of growth plans across most industries and functions, including marketing, R&D and the development of new technology.
 
Almost all organisations have a need for optimised data, and most mid-sized to large organisations require in-house data professionals, as well as smaller organisations that are data-centric. 

What does career progression look like for a data scientist? 

For an ambitious and skilled data scientist, the sky’s the limit. You can move-up the career ladder to more senior positions such as lead or chief data scientist, which will involve the management and leadership of other data scientists as well as interactions with other senior leaders and stakeholders.
 
Beyond that, if data is an integral part of an organisation’s business strategy, C-Suite positions are possible.

How can I become a Data Scientist in Australia? 

The path to becoming a Data Scientist begins with an undergraduate degree in either mathematics, computer science or information technology.  
 
An advanced degree (PhD or Masters) in a highly relevant discipline such as data science, statistics, engineering or comparable is then recommended. 
 
Having a comprehensive understanding of programming languages and linear algebra is a considerable advantage if you wish to pursue a career path in data science. 
 
There is a substantial amount of data science jobs in Australia. They are offered across a wide range of industries including finance, healthcare, mining, marketing, and information technology.