I am a native French data scientist. I started my career as a computer vision researcher in Japan, and then in my homeland. I am writing this from Stuttgart, Germany.
However, I am not working on German vehicle technology as you would expect. Instead, I discovered an amazing opportunity in the middle of a pandemic: A Stuttgart-based, AI-driven, image editing startup that focuses on automating digital imaging across all retail products.
I learned from my experience in Japan the difficulties of moving to another country for work. A point of entry in Japan with a professional network is often necessary. Europe is a great place to start because of its many cities. Paris, London and Berlin are often known for their diverse job opportunities, but also being hubs of specialties.
The pandemic has led to an increase in remote jobs, but it is worth expanding your search for opportunities that fit your interests.
Look for value in places you might not expect, such as retail.
I am a technology spin-off for a luxury retailer and apply my knowledge to product images. It was a problem I approached from the data scientist's point of view. I instantly recognized the potential value of a new application in a large, established industry such as retail.
Europe is home to some of the world's most iconic retail brands, especially in apparel and footwear. This rich history gives you the opportunity to work with billions upon billions of dollars of revenue and imaging technology. Retail companies have the advantage of having a steady stream of images to process, which provides an opportunity to generate revenue and potentially make an AI company financially viable.
Independent divisions within an R&D department are another avenue. There are many AI startups that work in a niche market, but they don't make a lot of money. This is due to the high cost of research and the revenue generated from clients who are very specific.
Companies that have data are companies with potential revenue
This startup was appealing to me because it offered data access. Data is expensive, and many companies only have a limited amount of data. Companies that engage directly at the B2B and B2C levels, particularly digital platforms or retail that impact front-end user interfaces, are worth looking for.
Leveraging such customer engagement data benefits everyone. It can be used to further research and development in the category. Your company can also work with other verticals to solve their problems.
This also means that there is huge potential for revenue growth if the brand has a broad reach. I recommend that you look for companies that have data stored in a manageable format for easy access. This system is beneficial for research and development.
Many companies don't have the resources or know how to implement such a system. Look for the chance to offer data-focused offerings if a company refuses to share their deep insights or has not implemented it.
Automating processes is the best option in Europe.
I am attracted to early-stage companies that allow you to build processes and core systems. My company was in its infancy when I joined it. It was still working to create scalable technology for one industry. Although the questions the team was assigned to solve were being answered, there were many processes that needed to be implemented to address a multitude of other problems.
My year-long effort to automate bulk photo editing taught me that if the AI you are building can run independently across multiple variables simultaneously (multiple image and workflows), then your technology will be able to do what well-known brands haven't been able. This is a rare feat in Europe and there are many companies that do it. They are also hungry for people who can.
Don't be afraid to experience some culture shock, and make the leap.
