I became a data scientist by accident: I followed my curiosity and did a PhD in computational linguistics and recommender systems. When I finished my PhD in 2012, I discovered I could call myself a data scientist rather than a software engineer with a research background (which was a bit of a mouthful). As 2012 was the year Harvard Business Review declared data scientist to be the sexiest job of the 21st century, I didn’t need to think much about what kind of data scientist I was. Just being a data scientist was pretty unique.
The world has changed in the past eleven years, and now there are many more data scientists. While you could earn good money as a generic data scientist, you don’t stand out. That is, it’s not only that software commodities are replacing interesting data science work, and that large language models are making some skills irrelevant – whatever is left of the core data science skillset has become an undifferentiated commodity.
I’ve been thinking a lot about positioning as an independent consultant recently, after realising that the lines between solo consulting and product building are blurry. One great source to learn more on the topic is Jonathan Stark, who has published many valuable resources over the years. Among them, I found a podcast interview he did in May this year with Genevieve Hayes, titled Building Your Authority in Data Science.
Whether you’re an employee or independent data scientist, it’s worth listening to the interview. Here are my key takeaways:
- Even though data scientists are already highly specialised, the problem of positioning oneself and standing out is common.
- Understanding marketing and the business side in addition to mastering the technical skills can be a superpower, as you can act as a bridge between non-technical people and the “nerds”.
- You need to be perceived as meaningfully different by your target audience, regardless of whether you choose to specialise in a horizontal (specific data science skill like computer vision) or in a vertical (specific industry like renewable energy). If your target audience doesn’t find you meaningfully different, you have more work to do.
- Avoid basing your self-worth on where you sit compared to other data scientists. If you’re good enough technically (C to B+) and you have complementary skills and an outcome-driven mindset, you’d be unstoppable. This still seems rare.
- Publish stuff that business people can understand, i.e., connect what you can do on the technical side with business value.
- You don’t need to be managing people to deliver results, e.g., Jonathan chose to remain solo and not hire employees. Focusing on business results is what matters.
- At the time of the interview (May 2023), Jonathan was searching for a ChatGPT consultant to learn whether he could turn his content into a chatbot. He was surprised he could barely find anyone. This is a good example of riding a hype cycle, as being an early authority on ChatGPT can lead to solid business outcomes for indie consultants. However, given the nature of hype cycles, this can change quickly.
- Do things you’re deeply curious about, as enthusiasm helps you stand out. Going down rabbit holes can be a strength. In the context of ChatGPT consulting, this reminded me of Simon Willison and Ethan Mollick, who have become more well-known recently due to their curiosity and blogging on generative AI.
- There are many ways to make money, so you might as well work on something you like.
Personally, I’m still figuring out my positioning. I found it interesting that Genevieve and Jonathan agreed that stereotypical data scientists care more about building their models than about how they’re used. While I enjoy the technical aspects of modelling and other data science tasks, I’m much more interested in shipping work that matters. That’s why I became a data scientist originally – I left academia after my PhD and joined startups to get close to business problems and build stuff people use. If that’s still a rarity, I suppose it can help with my positioning. That said, I’m also exploring a deeper vertical specialisation (currently looking at energy markets).
Public comments are closed, but I love hearing from readers. Feel free to contact me with your thoughts.