As opposed to the companion post about data science in a corporate, let’s talk now about the data science life at a startup or small company, that is, a company with less than 100 employees.
A startup has, by definition, an untested business model: the company runs on seed money and may or may not be profitable. In this setting, data science is a core differentiator that can, presumably, disrupt the market and generate infinite profits for investors. So what does a data scientist do there? As opposed to large corporates, a data scientist will work as part (or closely with) a product team. Since the company itself is still small enough, there are not many business processes to improve (typically non-existent), and since data science is a strategic choice, then it is natural for data scientists to be part of a product team. They can, sometimes, be also business analysts.
What to expect from working on a startup?
- Flexibility (home office, flex hours).
- Relaxed environment and work atmosphere.
- More modern tech stack and (current) best practices.
While this sounds like a great deal, I would argue that this is not the ideal place to start a career. Startups are fantastic places for experienced professionals, but too chaotic for juniors. I recommend spending at the very least 2 years in corporate first.
Other downsides of startups include:
- Unreal business model, non-existing profit: Many industries are no more than thin air.
- None of the co-founders is tech-savvy: Sadly, many healthcare startups.
- No data: Usually a corollary of the previous one. Due to some unrealistic expectations of non-technical co-founders, artificial intelligence is expected to learn “on its own”, somehow, magically.
- While this is maybe a personal thing, an unhealthy focus on “family” culture. I like my work relationships to stay where they belong, at work. Sure, sometimes your colleagues can get upgraded into friends, but that should not be the default mode.