How to Become a Machine Learning Engineer: Australian Careers in Data Science

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Certified Data Science Professional

Take your first step towards an exciting career in an accelerating global industry with this online Certified Data Science Professional course from Lumify Learn.

Designed for students with an interest but no prior experience in data science, this course is specifically designed to fast-track students to a new professional career. In this course, you’ll build the core skills, knowledge and connections required to succeed in the rapidly growing global field of data science.

With a particular focus on supporting students to be work-ready, this course offers valuable connections with future employers, and the tools you’ll use in your real-world work. You’ll gain four industry-recognised Microsoft Azure certifications, be supported and guided by expert mentors, and have access to cutting-edge resources and techniques. At the end of your course Lumify Learn’s unique Industry Partner Program connects you directly with the businesses looking to recruit frontline professionals at the end of your course.

Entirely online and self-paced delivery means you can complete this course at the pace that suits you. You could gain a globally recognised qualification in as little as three months, or complete this course at your pace within one year.

For an exciting new career in a growing field, this course is your next career step.

Online

Analyst, Data Analyst, Data Scientist, Database Administrator, Database Analyst, Machine Learning Engineer

Price may vary

Self-paced, up to 12 months part-time

Master of Data Science

Establish yourself as an expert in data science and be part of a growing in-demand field when you study the online Master of Data Science at UNSW.

The skills you develop in this program are broad and deep and apply across all fields and industries. Cover advanced statistics and machine learning, programming and database systems, plus strategic decision making and more. This course explores more ways to organise, identify, analyse and ultimately use data to inform strategies, redefine ambiguous questions and find answers that make a genuine impact.

Created and led by academics who are at the forefront of the industry, you can ensure that your online learning experience will be of the highest standard.

Designed with the busy individual in mind, this 100% online course allows you to learn each subject in seven week blocks, allowing you to accelerate your study and graduate in two years.

Pursue a career that’s dynamic and rewarding, starting with the Master of Data Science. Enquire today!

Online

Data Analyst, Data Engineer, Data Scientist, Database Administrator, Machine Learning Engineer, Market Research Analyst, Market Researcher

Price may vary

Accelerated; complete in as little as 2 years

Master of Data Science

Solve complex business problems and make an impact. Use data science and analytics to ask the right questions, build the right programs, and create powerful solutions–no matter your professional background.

In La Trobe’s Master of Data Science course, you’ll learn how to process complex data, apply statistical modelling, and use programming languages while studying 100 per cent online.

You’ll work with real data sets from our industry partners in our virtual lab supported by Microsoft. The lab allows you to practice and perfect your skills with open-source software and platforms, programming languages, Big Data and cloud-based services, so you graduate with hands-on experience.

Choose an industry-focused specialisation in Big Data and Cloud Computing or Data Modelling and Analytics.

You can complete the course in 14 months with combined skill sets that enable you to use Big Data to identify and solve challenges, manage programs that manage Big Data, or build a program from the ground up.

As a data scientist, Payscale reports that your average salary would be AU$91,857 per year, with the top 10 per cent earning AU$129,000. With skills in machine learning, Python, R and statistical analysis, the salary is higher. A graduate diploma and a graduate certificate are also available so that managers or professionals in a technical role can better understand the foundation of data science and make evidence-based decisions.

Online

Business Analyst, Data Analyst, Data Scientist, Database Analyst, Machine Learning Engineer

Price may vary

14 to 24 months

Graduate Certificate in Data Science

The online Graduate Certificate in Data Science at UNSW covers the foundational skills of data science as applied in computer science, economics and mathematics. It also includes programming, statistics and data systems.

The program provides you with the skills that employers demand in a fast-tracked model, plus gives you a pathway to study the Graduate Diploma in Data Science and Master of Data Science.

Courses are delivered online in seven-week intensive blocks, which means you could graduate with a Graduate Certificate in as little as 8 months.

Online

Data Analyst, Data Engineer, Data Scientist, Database Administrator, Machine Learning Engineer, Market Research Analyst, Market Researcher

Price may vary

Accelerated; complete in as little as 8 months

Graduate Diploma in Data Science

The online Graduate Diploma in Data Science at UNSW covers the foundational skills of data science as applied in computer science, economics and mathematics.

You’ll learn about important decision-making techniques, along with how to be a good data storyteller. The course also includes programming, statistics and data systems.

The fast track option means you could graduate with the diploma in 16 months. It also provides a pathway to study the Master of Data Science, should you continue to take it to the next level.

Online

Data Analyst, Data Engineer, Data Scientist, Database Administrator, Machine Learning Engineer, Market Research Analyst, Market Researcher

Price may vary

Accelerated; complete in as little as 16 months

On this page

What is a Machine Learning Engineer?

What will I do?

What skills do I need?

Resources

What is a Machine Learning Engineer?

A career as a Machine Learning Engineer is an exciting and dynamic path that sits at the intersection of technology and innovation. These professionals are responsible for designing and implementing algorithms that enable machines to learn from data, making it possible for systems to improve their performance over time without explicit programming. With the rapid growth of artificial intelligence and data analytics, Machine Learning Engineers play a crucial role in developing solutions that can transform industries, from healthcare to finance and beyond.

The responsibilities of a Machine Learning Engineer are diverse and engaging. They typically involve collaborating with data scientists to understand the data and the problems at hand, selecting appropriate models, and fine-tuning algorithms to achieve optimal results. This role requires a strong foundation in programming, statistics, and data analysis, as well as the ability to communicate complex concepts to stakeholders. Common tasks include preprocessing data, training models, and evaluating their performance, ensuring that the solutions developed are both effective and efficient.

In addition to technical skills, a successful Machine Learning Engineer must possess a problem-solving mindset and a passion for continuous learning. The field is constantly evolving, with new techniques and tools emerging regularly. This means that professionals in this area must stay updated on the latest advancements and be willing to adapt their approaches as needed. The collaborative nature of the work also fosters a sense of community, as engineers often work alongside other experts in data science, software development, and business analysis.

Overall, a career as a Machine Learning Engineer offers a fulfilling opportunity to contribute to cutting-edge technology while solving real-world problems. With a strong demand for skilled professionals in this field, individuals pursuing this career can look forward to a rewarding journey filled with growth, innovation, and the chance to make a significant impact across various sectors.

Career snapshots For Machine Learning Engineers

The role of a Machine Learning Engineer is increasingly vital in today’s technology-driven landscape, particularly in Australia. This profession is at the forefront of innovation, focusing on developing algorithms and models that enable machines to learn from data. As industries continue to embrace digital transformation, the demand for skilled professionals in this field is expected to grow significantly.

Average Age: Typically ranges from 25 to 40 years.
Gender Distribution: The field is currently male-dominated, but efforts are being made to encourage diversity.
Hours per Week: Generally, Machine Learning Engineers work around 38 to 40 hours per week.
Average Salary: The average salary for a Machine Learning Engineer in Australia is approximately AU$89,574, with a range from AU$59,000 to AU$142,000.
Unemployment Rate: The unemployment rate in this sector is relatively low, reflecting high demand for skilled professionals.
Employment Numbers: There are thousands of Machine Learning Engineers currently employed across various industries in Australia.
Projected Growth: The demand for Machine Learning Engineers is expected to increase significantly in the coming years, driven by advancements in data analytics and artificial intelligence.

As organisations continue to invest in technology and data-driven solutions, pursuing a career as a Machine Learning Engineer presents a promising opportunity for those looking to enter a dynamic and evolving field.

What will I do?

Machine Learning Engineers play a crucial role in the rapidly evolving field of artificial intelligence and data analytics. They are responsible for designing, developing, and implementing machine learning models that enable computers to learn from and make predictions based on data. As the demand for skilled professionals in this area continues to grow, Machine Learning Engineers are at the forefront of innovation, driving advancements across various industries. Their work not only enhances operational efficiency but also contributes to the development of intelligent systems that can transform the way we live and work.

Data Collection – Gathering and preprocessing data from various sources to ensure it is suitable for training machine learning models.
Model Development – Designing and implementing machine learning algorithms and models tailored to specific business needs.
Model Training – Training machine learning models using large datasets to improve their accuracy and performance.
Model Evaluation – Assessing the performance of models through various metrics and techniques to ensure they meet required standards.
Collaboration – Working closely with data scientists, software engineers, and other stakeholders to integrate machine learning solutions into existing systems.
Research – Staying updated with the latest advancements in machine learning and artificial intelligence to apply new techniques and methodologies.
Deployment – Implementing machine learning models into production environments and ensuring they operate effectively.
Monitoring – Continuously monitoring model performance and making necessary adjustments to maintain accuracy over time.
Documentation – Creating comprehensive documentation for models, processes, and methodologies to facilitate knowledge sharing and compliance.

What skills do I need?

A career as a Machine Learning Engineer requires a robust blend of technical and analytical skills. Proficiency in programming languages such as Python and R is essential, as these are commonly used for developing machine learning models. Additionally, a strong understanding of algorithms, data structures, and statistical analysis is crucial for effectively interpreting data and creating predictive models. Familiarity with machine learning frameworks like TensorFlow or PyTorch can also enhance a candidate’s capabilities, enabling them to implement complex algorithms efficiently.

Beyond technical skills, successful Machine Learning Engineers must possess problem-solving abilities and critical thinking skills to tackle real-world challenges. They should be adept at working with large datasets, requiring knowledge of data preprocessing and feature engineering techniques. Communication skills are equally important, as these professionals often collaborate with cross-functional teams to translate complex technical concepts into actionable insights. As the demand for machine learning expertise continues to grow, individuals equipped with these skills will find themselves well-positioned in a dynamic and evolving job market.

Skills/attributes

Strong programming skills in languages such as Python, R, or Java
Proficiency in machine learning frameworks like TensorFlow or PyTorch
Solid understanding of algorithms and data structures
Experience with data manipulation and analysis using tools like Pandas or NumPy
Knowledge of statistical analysis and probability
Familiarity with data visualisation tools such as Matplotlib or Seaborn
Ability to work with large datasets and databases, including SQL
Understanding of software development practices and version control systems like Git
Strong problem-solving skills and analytical thinking
Effective communication skills to convey complex concepts to non-technical stakeholders
Ability to work collaboratively in a team environment
Continuous learning mindset to keep up with evolving technologies and methodologies
Experience with cloud platforms such as AWS, Google Cloud, or Azure
Knowledge of ethical considerations in AI and machine learning

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How to Become a Machine Learning Engineer: Australian Careers in Data Science

What is a Machine Learning Engineer?

Career snapshots For Machine Learning Engineers

What will I do?

What skills do I need?

Skills/attributes

Resources

Career Outcomes