How Alex became a data scientist at Westpac after COVID-19 forced a career change

Marketing and mathematics to data science job - Alex Dance

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 AI while also leveraging his marketing domain knowledge. So he retrained in Data Science and AI with the Institute of Data and UTS 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 COVID-19. This is his journey so far:

 


Tell us about your career before making the change to data science?

“I started with a maths / statistics degree. My career has included managing a data analytics team, 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.”

What motivated you to upskill to data science, analytics and AI through our program?

“The data science course allowed me to quickly take my analytic skills to the next level. For many years now I have been interested in predictive analytics and data analysis and the way it can impact business and human behaviour, but 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.”

You’ve recently landed a new role with Westpac as a Data Scientist / Analyst – Congratulations! What does your role entail and what is the most challenging part?

“My days vary but the goal is to work with data and marketing teams to ensure we get the most value out of the data we have to 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 to see a better approach to solve complex problems.”

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 course, before moving to Westpac.”

How did you prepare for your data science interview?

“I prepared by reviewing my achievements from previous roles and I kept notes on what we were learning throughout the course including my capstone project work, which I reviewed before interviews. I also went through a lot of useful online resources and videos that helped.”

What are 3 tips you would give someone applying for jobs in the data industry after upskilling?

“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)”

What is it really like working in your new role as a data scientist?

“The data a student works with is usually too good, with an obvious success or fail. In the real world trying to determine success or failure is a lot more complicated.

Whenever I have started in a new role I have always felt a combination of confidence in my abilities plus apprehension that this will be a bit daunting. In this new role I found that my fresh eyes have added a lot of value, which is exciting! I’m also glad that I can add value and I have been brought into other useful side projects based on my skills and experience.”

What appealed to you about the Data Science 12-week Full-Time Program in particular? What was the most challenging aspect of completing the course / what did you enjoy the most?  

“I chose the full-time course because it gave me the ability to complete the program much faster and I was also ready to dedicate my time 9am-5pm Monday-Friday to complete my training. During the first few weeks I was interested in a few side projects, but the regular course work quickly become 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 better way than most analysts could even dream of.”

Which transferable skills did you have and how did they help you to learn data science, analytics & AI?

“I have been working with data for over 20 years, so I was able to see the insights from the data quickly. 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 really focus on how what I was learning could be applied on the job.”

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 gave me the opportunity 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.”

What are some tips you would give someone preparing to present their Capstone project during the data science & AI program?

“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.”

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

“Our trainer was a very good analyst, 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.”

How do you compare your career situation now 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 and use 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.”

What advice do you have for those looking to break into the data science job market – how can they set themselves up to do well?

“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 some 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.”

When did you know that data science was the right career path for you?

“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 wanted to gain the skills and knowledge that would make this possible.”

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

“I want to be able 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 better drive future campaigns.

My goal is to be able 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.”

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 up-skilling and landing a role in the Data Science industry, book a consultation with a Data Industry Career Consultant today.

 

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