How Alex Became a Data Scientist at Westpac After Covid-19 Forced a Career Change

From marketing & mathematics to data science Alex’s journey into tech.

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Father of two Alex Dance was busy running a successful marketing consultancy when COVID-19 hit.

With his clients abruptly cutting their marketing budgets, Alex was faced with a harsh reality and knew he needed to take action.

Fascinated by data, Alex wanted to remake his career by gaining practical skills in data science, data analytics, machine learning, and artificial intelligence (AI) while also leveraging his marketing domain knowledge.

So he retrained with the Institute of Data’s Data Science & AI Program over the course of three months and then landed a role at Westpac as a Data Scientist / Analyst.

Alex was mentored and trained by data science experts and quickly secured his new role in the high-growth data industry despite the challenges of COVID-19.

This is his journey so far:

1. Hi Alex, would you tell us about your career before making the change to data science?

I started out with a maths/statistics degree.

My career has included managing a data analytics team and running marketing campaigns for David Jones, CBA, and Woolworths.

I then moved to Optus, where I was running analytic capabilities before moving into the strategy team.

I then took the plunge and, for 4 years, ran my own analytic business offering specialist capabilities.

That business went from strength to strength until COVID hit.

2. What motivated you to upskill in Data Science & AI through our program?

The Data Science & AI Program allowed me to take my analytic skills to the next level quickly.

For many years, I have been interested in predictive analytics and data analysis and how they can impact business and human behaviour.

I knew there was a gap in my knowledge, and if I wanted to progress my career and take on a more data-focused role, I needed to build my skills.

3. You’ve landed a new role with Westpac as a Data Scientist / Analyst – Congratulations! What does your role entail?

My days vary, but my goal is to work with data and marketing teams to ensure we get the most value out of the data we have and roll out better marketing campaigns.

The challenging part is having to learn multiple systems, but there is a good amount of training to help support me.

When immersed in data analytics and data science for so long, every bit of extra knowledge makes it easier to push through and see a better approach to solving complex problems.

4. How did COVID-19 impact your career plans this year?

In a very short period, many of my clients completely dropped all of their marketing budget.

That was when I started and committed myself to the Data Science & AI Program before moving to Westpac.

5. How did you prepare for that role’s interview?

I prepared by reviewing my achievements from previous roles, and I kept notes on what we were learning throughout the program, including my capstone project work, which I reviewed before interviews.

I also went through many useful online resources and videos that helped.

6. What are 3 tips you would give someone applying for jobs in the data science industry?

My tips are:
• Apply online and, at the same time, proactively reach out (make a call) to the recruiter
• Have a Kaggle and GitHub portfolio
• Spend a designated amount of time every day applying for jobs (e.g. 2 hours a day).

7. What is it actually like working in your new role as a data scientist?

The data a student works with is usually of high quality, but in the real world, determining success or failure is much more complex.

Whenever I have started a new role, I have always felt a combination of confidence in my abilities and apprehension that it will be a bit daunting.

In this new role, I found that my fresh eyes added a lot of value, which is exciting!

I’m also glad that I can add value, and based on my skills and experience, I have been involved in other useful side projects.

8. What appealed to you about the Data Science 12-week Full-Time Program in particular? 

I chose the Data Science & AI Full-Time Program because it gave me the ability to complete the program much faster, and I was also ready to dedicate my time, 9 am-5 pm, Monday-Friday to complete my training.

During the first few weeks, I was interested in a few side projects, but the regular coursework quickly became the priority, especially because I wanted to really understand what was being covered and wanted to be able to apply what I was learning.

The biggest challenge was finding enough time to get everything done to the standard I was happy with, so I found myself regularly putting in thoroughly enjoyable extra hours every week.

I was keen to put in the time because I loved that I was quickly learning how to use data and extracting amazing insights in a way that most analysts could only dream of.

9. Which transferable skills did you have, and how did they help you in this process?

I have been working with data for over 20 years, so I could quickly see the data’s insights.

I also have a curiosity that drove me to further explore and delve into the fundamentals of what was being taught.

Furthermore, I have a passion for the big picture, for big data, and for identifying multiple insights; this mindset enhanced my learning experience and enabled me to focus on how what I was learning could be applied on the job.

10. Tell us about your Capstone Project. How did you find the process?

I chose a time series analysis and produced thousands of forecasts, which would have been impossible to do without AI.

I initially felt a bit daunted by the capstone project but as time went on I was really glad I completed something that was more in-depth.

The project also allowed me to focus, as there was potential to go down multiple paths and to try too much, but I was able to put together a plan and stick to it.

11. What are some tips you would give someone preparing to present their Capstone project?

I found it useful to storyboard the presentation weeks before it was due because it helped me understand what analysis was needed.

I also practised the presentation with my fellow students a few times in the week beforehand, so by the time we got to the final presentation it was easy.

We also threw around some ideas on potential questions, so the final questions/answers were also rehearsed, but we learned a lot working as a team.

12. How did the program change your perspective on the importance of soft skills in data science?

Our trainer was a very good analyst and teacher and also already had the soft skills we needed to learn.

Every day, I looked forward to learning from him.

Similarly, in a workplace, you work very closely with technical and non-technical people, and when they have relevant soft skills, then they are somebody I look forward to working with.

I also like working in teams where we all support and encourage each other, which is where having soft skills is important, and I think everyone should spend time developing their soft skills if they are looking to work in data.

13. How do you compare your career situation to 6-12 months ago?

12 months ago, I was a good analyst, but there are also lots of other good analysts around.

Now I have taken my skills to the next level.

I have relevant and valuable skills that I can utilise to add extra value for my key stakeholders.

Others (non-analysts) in the business have a basic understanding of the data options available to them but I can proactively present insights and make changes to the business that can make a big difference to the profitability.

Those skills are in demand, and therefore, I am more in demand!

14. What advice do you have for those looking to break into the data science job market?

Apply for jobs and get into a good organisation with potential and show off your skills of understanding data and making the most of what is possible.

When I had some spare time I worked on side projects to keep my analytic mind ticking over.

Projects I worked on included a) Stock market analysis for personal gain and b) Text analysis of documents.

I can now predict if a novel is any good, based on multiple word counts and sentiment analysis.

15. When did you know that data science was your right career path?

I always knew I was good at understanding data and explaining it to stakeholders.

When I started seeing a lot of press about data science, I knew that I wanted to be involved for the long term and gain the skills and knowledge that would make this possible.

16. Now that you’re trained with in-demand data skills, what’s your future career plan?

I want to go from running one-off models to a more automated process for both choosing the right model and running AI across every marketing campaign and then using the results to drive future campaigns better.

My goal is to improve the customer experience for as many people as possible.

When I can help provide the right information /offer valuable and previously unknown insights on a large scale, and improve lives, I am happy.

17. Do you have any final thoughts for people looking to work in data?

As long as you understand the data and work towards an end business goal, keeping the client’s needs in mind, things will work out well.

Connect with Alex on LinkedIn here.

If you are interested in a career in data science consider downloading a free copy of the Institute of Data’s Data Science & AI Course Outline.

If you would like to speak to a local team member about our programs, book a free career consultation.

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