24 hours with Meera, Data Scientist
Hello, I'm Meera, a Data Scientist for Orange Business, based in Oslo. I work with many different industries on multiple cloud projects at once, helping to find the most appropriate solutions to various issues. It's a research-based role, so deciding whether data science is even needed to solve a problem is usually the starting point of all my tasks. This involves a lot of reading, communicating, and brainstorming.
07:00
Before work, I like to try and get some exercise in. I'm very lucky to live close to the woods, so my morning runs tend to be pretty scenic, running along one of the many beautiful trails the Norwegian forests have to offer.
08:30
I get to work just in time to enjoy a caffè mocha, the perfect way to kickstart my day in the office.
09:00
It's time for our daily stand up. We talk about any project updates we have and the day's tasks. Today, we have a little extra time, so one of my colleagues demonstrates a Streamlit app they made.
09:30
Once the stand up is over, I get started on organizing my tasks and my readings so I can take notes from them. Since I find that I'm most efficient before lunch, I'll tackle the hardest task first.
10:00
I'm currently working on an "anomaly detection in manufacturing industry" project as part of the Data Driven Solution Taskforce. We're focusing on building generic solutions that could be targeted at a broad spectrum of customers using the cloud. We've divided a few algorithms between us to work on, so I'm focusing on mine this morning.
11:00
Time for lunch! Lunchtimes here in Norway tend to be quite early, which definitely took me a while to get used to, having moved over from southern India a few years ago. We have lunch as a team in the canteen and chat over food.
12:00
I'm back at my desk with a coffee in hand. I prepare for my meeting, where the team will discuss the pros and cons of the algorithms we've been working on, and ultimately pick the one that will best solve our problem.
13:30
I dive into the next task on my to-do list. I work with MLOps on cloud, and I've encountered a problem in the hyper scalers. I'll discuss the issue with either the Azure or the AWS team to try and get some help.
15:00
I've heard talk of cakes in the office, so I obviously have to leave my desk to investigate and see if the rumors are true.
16:00
It's the end of the workday! I head home on the subway, listening to music while taking in the views. Everything seems so much brighter and greener after the long winter we had – even the people seem happier.
17:00
Once I'm home, I take care of my plants. They kept me sane during the harsh winter, which I'm still no closer to acclimating to. I was used to almost constant summers back in southern India!
18:00
I start to think about what to have for dinner and settle in for a restful evening, ready to do it all again tomorrow.
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