5.30 AM
Tuesday morning force myself out of bed to quickly shower, get ready and jump in my car and head off at around 6am into the office. I work in the office on Monday, Tuesday, and Thursday, and from home on Wednesday and Friday.
7.30 AM
I usually give myself an hour and a half to drive, find a park and walk to the office. The moment I walk into the office I go to my locker, grab my keyboard, mouse and mug and find a desk, I have a couple of favourite hot desks that I prefer to sit at, each have their own positives like having a wall next to me or at the end of a row. I go and prepare my breakfast in the office kitchen and sit down to have breakfast.
While having breakfast I will log online to check any emails or MS teams messages that have come through after I had finished the previous day, GMHBA has great flexibility in allowing employees to start and finish at different times depending on our needs.
This is usually the perfect time for me to get my bearings on if there are any urgent requests that need to be added to me days plans or if they can be added to my to-do list. In my role requests can be anything from basic clarification on models that I have been working on, outputs/charts that are needed for certain meetings, to long term requests such as updating models based on the most up to date data available.
8.00 AM
Early mornings are where I like to get some of my more technical work done, many of the other employees coming into the office are just starting to arrive and I don’t usually have meeting set for the morning.
Currently I am working on the forecasting model to compare the previous forecasts to the current forecast which include more up to date data. The results from this will be directly distributed to the Head of Finance and the CFO as the basis for discussion in future meetings.
Most of the modelling work is done using the programming software R using machine learning models and previous data located in the data warehouse to give the most accurate predictions. I also use R to create reports from past data and forecasts output in slide decks or spreadsheets which get distributed to many different teams within the business.
9.00 AM
The finance crew usually goes out to grab a coffee for whoever wants one, can be a large or small group but it’s a great way to connect with colleagues early in the morning.
Tuesdays are great days for collaboration, it’s one of the days where you get the most people in the office at the same time and can collaborate in person as opposed to over MS teams which I certainly prefer. The mood is always upbeat here someone is always up for a chat and the open layout of the office doesn’t restrict any communication between staff.
10.00 AM
Presented some lapse analysis I had recently completed with colleagues from the retention team, having these open discussions/ meetings have been great for me to gain different points of view from within the business to further improve my work.
12:00 PM
Lunch time, time to get out and stretch the legs and go and grab a bite to eat, I’m usually not that well organised to have brought my lunch along with me so will often head across the road to the local Westfield and grab something from in there, certainly not short of options! Depending on the weather I will either go out for a bit more of a walk along the pier or beach.
2.00 PM
Weekly catch-up with my manager Brian, it’s a great opportunity to catch up on the work we’ve been doing, bounce ideas off each other, and generally check-in on how everything is going. I often like to use this as a chance to get feedback on any presentations that I am doing to improve the quality of my work.
3.00 PM
I now have time in my day to extract and provide some data for a couple of different people using SQL and R. These outputs in the form of spreadsheets are used by staff in the Acquisition and Retention Campaign team as part of their customer retention strategies.
Following this, I have been asked to prepare some extra outputs on the comparison between the previous forecasts and the new updated forecast. All the code for these tables is computed in R and saved as an excel spreadsheet and sent out to colleagues. I personally make sure that every R script used can be reproduced when newly updated data becomes available with no changes to the code.
4.30 PM
Time to head home, clean up the desk, pack my bag and place any things I don’t need to take to Work-from-Home in my locker ready for me the next time I come in. The long drive home is a nice way to de-stress from the day’s work before getting home. Wednesday, I work from home so certainly take advantage of the extra sleep-in.
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