- Tips for Aspiring Data Scientists
- Tips for Junior Data Scientists
- Tips for Senior Data Scientists
- Wrapping Up
Networking. We’re constantly told phrases like “I’ve heard networking is useful, why don’t you try that?”. Encouragements like this are well-meant, but not very useful. Most people understand the value of networking. Many data scientists are not hired through official job listings, but through referrals and connections.
It can be tricky to understand how to network effectively. Some advice is outdated, some is not relevant for data scientists, and some is outright wrong. It’s easier to look back at the previous step in your career and understand what actually worked. This is because you can see the common denominator between successful people around you ✨
In this blog post, I want to give you my tips for how to network as a data scientist. Below I’ve divided this into tips for aspiring data scientists, junior data scientists, and senior data scientists. I’m a senior data scientist myself, so I have experience with all of these steps. I’ve also talked with many professional recruiters and been on both sides of hiring interviews for data scientists. Still, please take everything I say with a grain of salt. I’m no more an authority on this topic than anyone else, and these are just my own reflections.
Before we start, you should know that I think approaching networking as a optimization problem is fundamentally flawed. Having 1000 LinkedIn connections or 5.000 likes to your post about LLMs is not the goal. The goal of networking is to establish genuine connections with other likeminded people that love the same things you do. These connections give you a network of people that you can both help and reach out to when needed.
Paradoxically, approaching networking as a optimization problem makes you bad at networking. This makes you focus on metrics rather than people. Meeting someone that only…