Data Scientist

A big part of a Data Scientists everyday job is to collect, clean, analyse and visualise organisation’s data – at CommBank this includes financial and customer data to help provide better insights and advice. Let’s explore what all of these terms mean in the role of a Data Scientist.

But what do all these terms mean when it comes to treating data? 

• Collecting data

  Data Scientists use specially designed software that ‘pulls’ the data from all the different sources (such as mobile banking, transaction histories and statements) and brings it together into one resource. 

• Cleaning and organising data:  

  This is one of the most important parts of a data scientist’s job. After all, poor quality data, such as data with errors or duplicates could lead to you making incorrect decisions down the line. Data cleaning is the process of making sure any glitches or errors are taken care of using special programs and processes. 

• Analysing data:

  Data scientists at banks use all of this data to create a ‘model’, or a profile of an individual account, a demographic (think a category of people such as women aged 18-22), an industry, or even an entire country’s economy. This helps them figure out important insights and predictions that can improve the financial wellbeing of their customers and communities. 
  
  These models can be assisted using cutting-edge technologies such as machine learning and AI to help see things humans would have a hard time detecting, like rapid fraud detection. 

• Visualising data:

  As they say, a picture speaks a thousand words – but data visualisation can tell an even bigger story. When a data scientist receives their data, it’s most likely going to be in a big list or spreadsheet that is difficult to read and understand, but by visualising data, you can detect patterns you would have a hard time seeing otherwise, which can help you understand and communicate ideas more effectively.