▲圖片標題(來源： Warit Silpsrikul/EyeEm/Getty Images)
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 across all retail products.
My experience 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 advantage here thanks to its many accessible cities. Cities like Paris, London, and Berlin often offer diverse job opportunities while being known as hubs for some specialties. While there has been an uptick in fully remote jobs thanks to the pandemic, extending the scope of your job search will provide more 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 application for a very large and established industry like retail.
Europe has some of the most storied retail brands in the world — especially for apparel and footwear. That rich experience provides an opportunity to work with billions of products and trillions of dollars in revenue that imaging technology can be applied to. The advantage of retail companies is a constant flow of images to process that provides a playing ground to generate revenue and possibly make an AI company profitable.
Europe has some of the most storied retail brands in the world — especially for apparel and footwear. That rich experience provides an opportunity to work with billions of products and trillions of dollars in revenue that imaging technology can be applied to. The advantage of retail companies is a constant flow of images to process that provides a playing ground to generate revenue and possibly make an AI company profitable.
Another potential avenue to explore are independent 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 cost 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 because of the potential access to data. Data by itself is quite expensive and a number of companies end up working with a finite set. Look for companies that directly engage at the B2B or B2C level, especially retail or digital platforms that affect front-end user interface.
Leveraging such customer engagement data benefits everyone. You can apply it towards further research and development on other solutions within the category, and your company can then work with other verticals on solving their pain points.
It also means there’s massive potential for revenue gains the more cross-segments of an audience the brand affects. My advice is to look for companies with data already stored in a manageable system for easy access. Such a system will be beneficial for research and development.
The challenge 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 company isn’t willing to share deep insights during the courtship process or they haven’t implemented it, look at the opportunity to introduce such data-focused offerings.
In Europe, the best bets involve creating automation processes
I have a sweet spot for early-stage companies that give you the opportunity to create processes and core systems. The company 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 already being 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 bulk image editing taught me that as long as the AI you’re building learns to run independently across multiple variables simultaneously (multiple images and workflows), you’re developing a technology that does what established brands haven’t been able to do. In Europe, there are very few companies doing this and they are hungry for talent who can.
So don’t be afraid of a little culture shock and take the leap.
轉貼自： TechCrunch
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