Lessons from my most recent Internship

I just finished my three month internship as a data scientist at an investment fund. And I want to be honest with you about what that experience was actually like, because I think there are a few things worth sharing that you don't usually hear.

I had never worked as a data scientist before. I had done projects, taken courses, built things on my own but walking into a real fund, surrounded by people who had been doing this for years, was a completely different feeling. The first few weeks I was constantly second-guessing myself and my work. I felt like a complete Imposter at some times.

Over time I realized something: everyone feels like this. Not just the interns. People with years of experience still have days where they feel like they are figuring it out. The difference between people who succeed and people who don't is that they are comfortable with not knowing all the answers to everything. You don't have to pretend you know everything. Be curious. Ask good questions. Learn fast. That's it. That's the job.

Everybody knows networking is important but networking comes in many different forms. My best connections didn’t come from officially scheduled welcome chats or meetings. It was just being genuinely open and friendly with the people around me. The other interns, the full-time associates, the partners.

Towards the end of my internship, I got a connection to another fund because of the work I had done. They literally made the connection and told me they would refer me as a good candidate for their position. That doesn't happen if you keep your head down and only focus on the work in front of you. The work matters, yes. But so does being someone people actually want to recommend.

So if you're about to start an internship or a new job: be nice to everyone. Be curious about people, not just about the work. And don't underestimate how much the people around you can open doors for you.

A quick one for the technical people reading this.

I got to work with Dagster for data orchestration and dbt for data transformation as part of our data pipeline. I also picked up CI/CD basics using Google Cloud and GitHub. All things I had heard of but never properly used in a production environment.

But the biggest thing I noticed wasn't a specific tool. It was a shift in what actually matters. AI is handling more and more of the coding and syntax work. What becomes the real skill is system design and architecture. Knowing how the pieces fit together, how data flows, how you structure a pipeline that is actually maintainable. The strategic angle of the data scientist is becoming way more important than being the best at writing code.

I think that's going to be the defining skill of the next few years in this industry. Not who can write the most code. But who understands how to build things that actually work at scale.

If you want to get into data engineering or data pipelines, start here:

Both are free to learn and look great on a CV.

Have a great week,

Chris