Career I – Altis Consulting

I’ve finished my Monash University Graduate Diploma of Data Science and I’ve got a full-time analytics job after a 6 month internship! Here’s how I did it…


In 2011 I completed the  Higher School Certificate (HSC) with an ATAR of 99.85 [1]

In 2015 I graduated a Bachelor of Mathematics (Advanced) from University of Wollongong with a WAM of 93.

In 2016 I worked part-time whilst continuing my research  in computational knot theory. At the end of 2016 I was accepted into a Masters of Philosophy (Science) at Sydney University to continue my research but couldn’t afford to move to the big city and couldn’t get proper funding.

The next stage of my research involved big data and parallel computing so I began investigating data science and decided to retrain, both for research purposes and employment.


The first set of blogs on this website, ‘Big Data I – V’, summarise my research for getting into the workforce in data science, analytics, business intelligence etc.

Big Data I – Tools of The Trade looked at 10 US, 10 UK and 10 Australian data science jobs on and clustered the skills, tools and knowledge needed in this field.

Big Data II – That’s Not A Tool compared these tools and skillsets to find out which ones were the most important to focus on learning.

Big Data III – Degree or Not Degree? Compared the best MOOCs (Massive Online Open Courses) and best US, UK and Australian Universities for learning data science.

The two main Australian universities I considered were

  1. Monash University – Graduate Diploma of Data Science (1.5 years part-time online)
  2. Australian National University – Masters of Applied Data Analytics (1.5 years full-time on-campus)

Both had excellent reputations and courses but I chose the Monash University GDDS online, partly so I had some free time to supplement university learning with extra learning via MOOCs, Udemy, Kaggle etc and also hopefully find a data science/analytics based internship during my studies. I found from Big Data III that while technical skills are a must, business acumen and consulting experience are very important for real data science jobs, but are often not taught in university courses.

How then to prepare for an internship? Did I have to be a data science unicorn just to get my foot in the door? The next blog post looked at the spectrum of data science jobs from data analysts all the way to unicorns.

Big Data IV – Unicorn Or Safety In Numbers? Comparing data science all-in-one unicorns to data science teams with analysts, engineers, computer scientists etc.

Next it was time to prepare for the interview itself.

Big Data V – Learning The Lingo Investigates the key terms and topics for each of the different disciplines of data science (statistics, computer science, business intelligence etc) and looks at some data science interview questions.

Around the same time I attended the 2018 Kaggle CareerCon online and heard a lot of advice from people in industry about how to structure your CV and prepare for job interviews. Plus it was a great way to network with like minded aspiring data scientists from around the globe. You can checkout the CareerCon videos here [2].

Some extra advice I found from all my job applications and interviews around this time:

  1.  Analytics jobs often want relational database (SQL) and/or data warehousing skills – ie they want a ‘data-person’ to sort the data, analyse the data and present the data all-in-one
  2. Creating simple appealing dashboards via Tableau, Qlik or Power BI was more in demand than machine learning models and high powered neural networks – ie many companies are still trying to understand their data and what it says rather than leveraging it for something exotic

Altis Consulting

altis website pic

One of the companies that offered me an internship around June 2018 was Altis Consulting. They specialise in data warehousing, business intelligence and analytics for both business and government across Sydney, Melbourne and Canberra in Australia, London in the UK and Auckland in New Zealand.

Perfect internship and first analytics job for a grad!

The data warehousing and dashboard visualisation tasks helped fill in some gaps from the R/Python centric Graduate Diploma of Data Science at Monash and I got first hand consulting experience working in a government department for the last two months of 2018.

There are over 100 IT Consultants at Altis and we regularly collaborate and help each other with the different skills and toolsets needed to give our clients the best results. You’re instantly plugged in to a wealth of knowledge and experience vastly greater to your own. Again, perfect job for a grad!

They keep up to speed with the latest trends and technologies and have direct connections with many important IT companies like Microsoft and AWS. There are regular meetings discussing what’s new and on the horizon.

Although not strictly an AI company, some other Altis consultants in Canberra successfully implemented two computer vision projects during my 6 month internship:

  1. Seed Classifier app  – use a smartphone to identify and classify seed types. Useful for agricultural businesses investigating AI, IoT and auotmation [3]
  2. Skin Cancer Detection app – use a smartphone over a patch of skin to estimate the likelihood of skin cancer. This was an ACT GovHack 2018 project and two of our consultants won ‘Spirit of GovHack’ awards [4].

Often aspiring data scientists can get too caught up in the theory, the algorithm or the accuracy to turn their projects into a commercial success. Here at Altis, the consultants are innovative enough to explore and achieve in the AI-sphere but also lucrative and realistic enough to generate an end product that provides real world benefits to its users.


2018 was a great year. 2019 looks even better. There’s a lot going on in AI and data science and I’ve fallen on my feet with a great first job to get involved.

All the best to all you aspiring data scientists and uni grads! Hope some of this was helpful. If you have any questions, or would like to connect, here’s my LinkedIn [5].







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