It’s a profession that’s growing in popularity and necessity with recent technological advances and one that is often regarded as a future-proof job.
According to Masters in Data Science: “A data scientist's role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans.”
A day in Data Science
Mathias Baltzersen, who's been with the company since the beginning, is Senior Data Scientist at raffle.ai. Mathias took time from his busy schedule to give an insight into what a normal work day looks like in this exciting and emerging field.
I bike my way through Copenhagen to the office at Toldbodgade, where I usually start my day with a cup of coffee while I chat with my coworkers. We often discuss experimental results if something has been “cooking” overnight.
We have a data science team stand-up meeting in the morning where we first discuss stuff we have been working on the previous day. It's mostly if we have anything we want to discuss or get feedback on.
This usually takes around half an hour because we go in-depth with what each of us is working on. After that, we have the “Scrum of Scrums,” which is usually a quick tour of what each team is working on and if there is anything blocking us.
We make our initial plans for the coming sprint midway through the current sprint and do the final planning right before the next one kicks off.
We usually have goals set, and the final sprint planning is discussing the tasks and breaking them down as much as possible, who might take which tasks, and so on.
At lunchtime, we’re usually mixed up with the other teams, so it's an opportunity to talk to them.
We talk about anything really, sometimes it's work-related, but more often than not, we talk about other stuff.
I have been at Raffle since the beginning of the company, so it's really exciting to see its progression and how the company is taking shape in its continuous development.
In the afternoon, I code, have meetings, discuss coding issues, or read the latest research papers.
The data science team has a good mix of concrete machine-learning engineering tasks and more research-orientated tasks.
Research tasks usually stretch over multiple sprints, whereas Machine Learning engineering tasks fit better in the regular scrum format.
Most of the team works with a mix of both, but we keep each other in the loop for the research-oriented tasks. It's mostly one person coding while everyone else decides on the direction and contributes to interpreting the results.
The best thing about working for raffle.ai is that we work on fascinating problems on the frontier of the newest research in the highly progressive fields of NLP and AI. We get to work with these technologies and try to make the research work in practice — that’s very exciting.
Outside work, I really like to do active stuff. During summer, I like to wakeboard, which a few of us in Raffle do, so we go together sometimes. During winter I like to play badminton.
A lot of people at Raffle like to do bouldering, so there is usually an informal company excursion once a week or so where people go climbing.