Member spotlight: Dr Alex Antic, IAPA Top Analytics Leader for 2021

21 July 2021


  • Did you know that one of Australia’s top Analytic Leaders is a ACS Canberra Branch member?
  • Dr Alex Antic, most recently from the ANU and now consultant, was named as one of these leaders.

In June, the IAPA announced the 25 people at the top of analytics leadership.  Dr Alex Antic, most recently from the ANU and now consultant, was named as one of these leaders.  ACS Canberra sat down with Alex for a conversation to get to know him a bit better, about the data science, analytics and its future, and why he is involved with the ACS as a volunteer through the Technical Advisory Committee.

What is the back story of Dr Alex Antic?  What spurred your interest in data science?

I think that it is fair to say that it was natural and inevitable that I'd work in Data Science given my formal studies in mathematics and computer science. Coupled with enjoying solving complex problems and helping people, I felt that it was the ideal career for me.

However, my career trajectory has somewhat been unusual: I began my journey in academia, moved to the financial services industry, then to government, back to academia, and now have my own consulting, advisory and training business.

The nature of my work has always involved trying to make sense of the world via modelling. I started off doing a PhD in applied mathematics, which involved modelling heat transfer in grain silos to combat insect infestations. Upon graduation, and after a short stint as a Post Doc, I began my career as a quantitative analyst - modelling complex financial derivatives for retail and investment banks, and portfolio optimisation in hedge fund investments). 

Once Data Science emerged as a field, it was an organic move, as it was effectively a rebranding of what I was already doing.

You’ve worked across a lot of different sectors and projects. What is it about data science that connects industry, government and academia?

The same principles apply across all industries and domains, that is: leveraging data and technology to enable evidence-based decision making. The aim is to help make better decisions, and given how much data most organisations collect and have access to, using data to drive decision making is hugely beneficial.

By consulting, advising, and training senior executives across industry, government, start-ups and academia, it's become clear to me that they're all just trying to figure out how to align technology with strategy - and that's what I really enjoy doing, working with people to solve complex problems that deliver measurable value.

Tell me about what it means to be recognised as one of Australia’s top analytics leaders?

I am truly humbled and honoured by the accolade, and it is a great opportunity to further promote the value of analytics and Data Science broadly.

I believe that as an analytics leader, it is my responsibility to advocate for more diversity and inclusion in the field, which is something that is very important to me.

It is also a fantastic opportunity to help educate and excite people about Data Science and AI via various speaking and training opportunities, and to hopefully tempt more young girls and boys to pursue a STEM-based career - Australia needs them!

What has been your greatest achievement to date?

I'd have to say my mentoring program, and sharing my experience and expertise broadly. Some of my mentees have gone on to achieve amazing and successful careers and to affect real change for the organisations they work for. 

Beyond that, I am really driven by helping others, and using my knowledge of all things data and tech to make their lives/work easier and better.

What’s your assessment of the current state of data science? Where is the future for data science?

The popularity of data science continues to grow in popularity - from uptake in industry and government, to increasing demand for upskilling and learning. Many people I work with want to understand how it can benefit their specific domain, and how to either get started, or successfully scale their existing capability.

I am seeing increased growth in data engineering, as more and more organisations productionise models, and thus need expertise in creating a scalable, repeatable and manageable processes.

As more and more of the mechanics of Data Science becomes automated and commoditised, it is becoming even more important for Data Scientists to deeply understand the domain they work in, focus on building collaborative working relationships, understand the ethical ramifications of their work, and to communicate with influence to actively support decision making. 

What is the state of legislation surrounding data science? 

During the past few years, I have seen conversations amongst senior executives shift from a focus on the technological aspects of Data Science and AI, to ethical and societal discussions. This has mainly been prompted by increasing societal/customer/shareholder/regulator demand for the responsible use of Data Science and AI.

Many organisations are realising that having a governance process and framework to support Data Science, AI and intelligent automation - especially AI ethics - has become mandatory. In Australia, there is growing regulatory focus on the technological operations of organisations, especially those whose analytics-powered solutions result in direct impact on their customers and society.

Some of the advisory work I do is focused on helping guide senior executives on the establishment of robust Data Science and AI governance frameworks, that incorporate AI ethics and human rights into strategy and operations. The pressure for organisation to develop ethical, fair, robust, explainable and responsible AI-powered solutions will only continue to increase.

What does your involvement in the ACS mean to you?

It is an honour to serve on the Technical Advisory Board (Data Sharing Committee), and to work with Dr Ian Oppermann (and other senior ACS executives), to help advance and promote the formulation of effective policies on ICT, data sharing and AI.

As a member of the Data Sharing Committee, it is a great opportunity to provide strategic advice, share my expertise, and to work with my immensely talented and experienced fellow board members in helping government and industry understand the importance and capabilities of effective data sharing. 

This includes guiding how to leverage emerging technologies, such as Privacy Enhancing Technology (PET), to enable privacy-preserving data sharing. PET allows organisations to better leverage data to create value for citizens and customers within the bounds of social licence, regulation/legislation and public good, such as via secure public/private data sharing.

What would you tell people who are interested in moving into data science?  What should be their first step?

It is an exciting field to work in, especially if you are curious, enjoy problem solving and making a difference.

I would suggest investing time and effort in developing your mathematics, statistics and coding skills, and trying to tackle some real-world problems to get a feel for the field - via internships, hackathons, or partnering with a Data Scientist in your current organisation. 

Beyond that, I would strongly recommend finding a mentor, developing your networking and communication skills, and focusing on learning as much as you can early in your career.

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