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Data scientists: don’t be afraid to explore new avenues

I’m a native French data scientist who cut his teeth as a research engineer in computer vision in Japan and later in my home country. Yet I’m writing from an unlikely computer vision hub: Stuttgart, Germany.

But I’m not working on German car technology, as one would expect. Instead, I found an incredible opportunity mid-pandemic in one of the most unexpected places: An ecommerce-focused, AI-driven, image-editing startup in Stuttgart focused on automating the digital imaging process wideness all retail products.

My wits in Japan taught me the difficulty of moving to a foreign country for work. In Japan, having a point of entry with a professional network can often be necessary. However, Europe has an wholesomeness here thanks to its many wieldy cities. Cities like Paris, London, and Berlin often offer diverse job opportunities while stuff known as hubs for some specialties.

While there has been an uptick in fully remote jobs thanks to the pandemic, extending the telescopic of your job search will provide increasingly opportunities that match your interest.

Search for value in unlikely places, like retail

I’m working at the technology spin-off of a luxury retailer, applying my expertise to product images. Approaching it from a data scientist’s point of view, I immediately recognized the value of a novel using for a very large and established industry like retail.

Europe has some of the most storied retail brands in the world — expressly for wardrobe and footwear. That rich wits provides an opportunity to work with billions of products and trillions of dollars in revenue that imaging technology can be unromantic to. The wholesomeness of retail companies is a unvarying spritz of images to process that provides a playing ground to generate revenue and possibly make an AI visitor profitable.

Another potential thoroughfare to explore are self-sustaining divisions typically within an R&D department. I found a significant number of AI startups working on a segment that isn’t profitable, simply due to the forfeit of research and the resulting revenue from very niche clients.

Companies with data are companies with revenue potential

I was particularly attracted to this startup considering of the potential wangle to data. Data by itself is quite expensive and a number of companies end up working with a finite set. Squint for companies that directly engage at the B2B or B2C level, expressly retail or digital platforms that stupefy front-end user interface.

Leveraging such consumer engagement data benefits everyone. You can wield it towards remoter research and minutiae on other solutions within the category, and your visitor can then work with other verticals on solving their pain points.

It moreover ways there’s massive potential for revenue gains the increasingly cross-segments of an regulars the trademark affects. My translating is to squint for companies with data once stored in a manageable system for easy access. Such a system will be salubrious for research and development.

The rencontre is that many companies haven’t yet introduced such a system, or they don’t have someone with the skills to properly utilize it. If you finding a visitor isn’t willing to share deep insights during the courtship process or they haven’t implemented it, squint at the opportunity to introduce such data-focused offerings.

In Europe, the weightier bets involve creating automation processes

I have a sweet spot for early-stage companies that requite you the opportunity to create processes and personnel systems. The visitor I work for was still in its early days when I started, and it was working towards creating scalable technology for a specific industry. The questions that the team was tasked with solving were once stuff solved, but there were numerous processes that still had to be put into place to solve a myriad of other issues.

Our year-long efforts to automate zillion image editing taught me that as long as the AI you’re towers learns to run independently wideness multiple variables simultaneously (multiple images and workflows), you’re developing a technology that does what established brands haven’t been worldly-wise to do. In Europe, there are very few companies doing this and they are hungry for talent who can.

So don’t be wrung of a little culture shock and take the leap.

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