Data scientists: Don’t Be Afraid to explore new Avenues

Data scientists: Don’t Be Afraid to explore new Avenues

I’m a native French data scientist who began his career as a computer vision research engineer in Japan and then in my home country. Despite this, I’m writing from Stuttgart, Germany, an odd center for computer vision.

But, contrary to popular belief, I am not working on German automobile technology. Instead, in the midst of the pandemic, I discovered a great chance in one of the most unlikely places: In Stuttgart, an e-commerce-focused, AI-driven image-editing business aims to automate the digital photography process across all retail products.

My time in Japan educated me about the challenges of working in a foreign nation. It is frequently required in Japan to have a point of entry with a professional network. Europe, on the other hand, has an advantage because of its numerous accessible cities. Cities such as Paris, London, and Berlin are regarded as hotspots for certain specialties while also offering a varied range of work prospects.

While the epidemic has resulted in an increase of entirely remote positions, broadening the breadth of your job search will yield more options that match your interests.

I’m working for a premium retailer’s technological spin-off, where I’m applying my experience to product photographs. When I approached it from the perspective of a data scientist, I quickly saw the promise of a fresh application for such a large and well-established business as retail.

Europe is home to some of the world’s most legendary retail brands, particularly in the textile and footwear industries. With so much experience, you’ll be able to work with billions of products and trillions of dollars in revenue using imaging technologies. The advantage of retail enterprises is that they have a steady influx of photographs to process, which provides a revenue stream and the potential to make an AI company lucrative.

Independent divisions, which are often found within an R&D department, is another option to consider. I discovered that a large percentage of AI firms are focusing on a market that isn’t lucrative, owing to the high cost of research and the money generated by highly niche clientele. This firm piqued my interest because of the possibilities for data access. Data is expensive on its own; so many businesses choose to work with a limited collection. Look for B2B or B2C enterprises that have a direct impact on the front-end user interface, such as retail or digital platforms.

Everyone benefits from utilizing such customer engagement data. You can put money toward more research and development in the category, and your company can then collaborate with other verticals to solve their problems.

It also indicates that the more cross-segments of an audience a brand influences, the greater the potential for revenue benefits. My recommendation is to seek organizations that have data stored in a manageable format that can be accessed quickly. Research and development will benefit from such a system.