A day in the life of a data scientist

Hi, I’m Sinem

I joined InVision in October 2018 as a Data Scientist - in fact, this is my first job since relocating to Germany approx. 1.5 years ago.

Before even thinking about looking for a job, I decided I needed to learn the language first. Even though I’m working in the tech industry, where English is the common language, I felt I needed to know the language of the country I am living in. And despite feeling confident, after having studied German over the past 8-9 months, taking a job interview in German is a whole different ball game.

InVision and me

The entire recruitment process, from submitting my application, the telephone and in-house interviews and the trial day, up to signing the contract, didn’t take more than 3-4 weeks and went very smoothly.

The first few weeks as a Data Scientist were pretty much about getting to know the “InVision system”, forecast algorithms used currently and gradually attending to daily business such as solving customer problems about forecasting.

In my previous position as a Senior Software Engineer in the US, from where I had relocated, “language barriers” obviously didn’t exist. But apart from that, my work at InVision differs quite a bit. Since I’m working in an agile environment here, I’m able to assess the impact of my work more or less instantly. Moreover, I particularly value the open communication we practise around here and the fact that decisions are taken jointly by the team based on a general consensus.

Also since our Data Science team is comparatively small to other teams, it’s important to me to be part of an environment that encourages me to voice my opinion freely. This especially applies to giving and receiving feedback - positive or constructive - as it’s purely a reflection of the general notion.

Sinem and Marc from our Data Science team.

Day-to-day routine - not really

After being with the company for nearly 10 months now, I can say the challenge is still there - just as much as the fun.

I enjoy my profession day-by-day, mainly as I’m able to grow personally and professionally. A “typical” day at work can be broken down into the following areas: Analyzing time series data in order to determine improvements on accuracy; spotting problems customers are facing; translating customers’ use cases in a quantitative and systematic manner; developing machine learning algorithms, from data cleaning to making predictions at the end.

Bottom-line: Although I had no idea of InVision until applying with them, my job as a Data Scientist here is pretty much what I was looking for.

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