Graduate profile image- Darshika
A day in the life of…

Graduate – Analytics and Information team, Technology department, Commonwealth Bank

Darshika Fernando studied a Master of Information Technology (Data Science) (Artificial Intelligence) and is a graduate in the Analytics and Information team, within CommBank’s Technology department.

Darshika Fernando

5:30 AM

It’s time to get started with the day. I usually set my alarm to go off at 5.30 am and then again at 6.00 am, just so I can experience that sweet ‘30 more minutes in bed’ thing. I am a creature of habit when it comes to my morning routine, so next it’s time for a quick shower and then I get ready, fix breakfast, pack lunch and leave home by 7.00 am, in that order.

7:00 AM

Time to leave home and walk to the train station which is about five minutes away. Then it’s a 50-minute commute to work. During this time, I read a couple of Medium articles on AI and machine learning, which I have bookmarked. This is one of the ways I keep up to date with the latest developments in the area I work in. 

I’ve developed an interest in the tiny house movement and I’m following one YouTube channel on tiny houses religiously. So, I check out if they have posted any new videos and spend the rest of my commute watching them.

8:00 AM

My walk from Town Hall station to the office is only a few minutes long. My team is extremely flexible in terms of working hours. I am one of the early starters of the day. This gives me some very quiet time early in the morning, where I go through my emails and check the calendar for all the meetings scheduled for that day.

My work is very much project oriented and structured using the agile methodology. In my capacity as a machine learning engineer, what I do could be summarised as coding, testing and productionising (essentially moving machine learning models to an environment where real users interact with the product). As a machine learning engineer, I’m focused on solving a specific data science problem where programming is simply a means to solve the problem.

10:00 AM

We have a conference call with one of the vendors for a project I’m working on. The objective of the meeting is to get solutions for issues related to some functional and non-functional capabilities of the product from this particular vendor. From CommBank, we have three engineers including myself, two data scientists and the solution architect for the product. When I emailed the vendor I got all of the questions we needed answered, so we can get started on the project right away.  

We have our daily stand up during this time too. Our Scrum team get together and provide an update on where we are at with certain tasks, if each of us are facing any blockers and what we are planning to do. This is crucial for the Scrum team to track where we are as a team in order to achieve the sprint goals we set up at the beginning of our two week sprint and to make any adjustments needed.

11:30 AM

I’m back at my desk, working through the task I started in the morning. I make a pull request to the repository of the software package to add any changes. Similar to any software development team, we have a matured process of any updates to our code bases being peer reviewed by multiple people before the updates are added. I receive feedback from the reviewers, who provide suggestions to improve the efficiency of the code as well as point out any mistakes that I have made. This feedback is extremely important because it ensures that the code we write is of high quality and provides a great learning opportunity for all parties involved.

12:30 PM

Time for lunch and to clear my mind.  This is the time I usually meet all the other graduates based around the office because everyone tries to sit together for lunch and to have a chat. We try to avoid talking shop most of the time during lunch, but sometimes the conversation does venture into something work related.

2:00 PM

It’s time for my quick bi-weekly catch up with my line manager. Since most of my team work on different projects, this is a good time to catch up and update him on where I am with my work. It’s usually a very casual chat. My manager encourages us to be upfront about anything we want to call out, and I use this time as a mentoring session where I get advice about where I want to be as graduate, what I have to improve on and how I could make the improvements in my current area.

3:00 PM

I follow through on the actions discussed during the daily stand up with regard to the vendor engagements. This includes verifying the information the vendor has provided through emails and during the conference call earlier in the day. I work with a data scientist to verify the information about the functional capabilities of the product and then do the verifications on non-functional capabilities myself. I document all the testing and validation I’ve conducted and shared this with the Scrum team for review. Once this is done, I do a quick check of my emails to make sure I don’t have any urgent emails left unopened until tomorrow.

5:00 PM

It’s time to head off for the day. My team has a dedicated working area, so I don’t have to clear my entire desk before leaving. I navigate through the crowd to Town Hall station and get into the train. On the commute back home, I listen to music and try to rest my eyes by not staring at my phone.

6:00 PM

Once I get home, I go for a run to unwind from the day, take a shower and start fixing dinner. Depending on the mood for the day, dinner could be something very quick and easy or a complicated recipe that I want to try out.

If I’ve signed up for a Kaggle competition that week, I work on that too. Kaggle is a platform for data scientists and developers to try out their machine learning skills. 

Irrespective of what I do for the evening, I try to go to bed by 10.00 PM so I can start the next day nice and fresh!

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