A few weeks ago I ended up doing a podcast collaborated with Towards Data Science, a medium publication that aggregates posts on everything related to data science and machine learning.
You can listen to full podcast here. We mainly talked about my career starting from entering data science as a new grad without any experience to working at a few different startups. I wanted to outline a few points that were made during the podcast.
- If you’re trying to break into data science, your brand helps your career a lot. I struggled to get jobs until I actually started blogging about data science. Ultimately, my blog landed me an interview, which led to an offer despite the fact that I failed the coding challenge that I was given.
- Employers want to see signs of genuine interest, even passion, from applicants. All the data science hype of the mid-2010s has led to a flood of passive enthusiasts who companies have to weed out, so signs of passion are great ways to show you’re serious.
- Working at an early stage startup means you have to be much more pragmatic, and wear many hats. When I joined Jobr, I wasn't doing “data science” things like cleaning data, building pipelines and training models. I was doing mostly data engineering work, building APIs, and eventually working on data science type machine learning problems when they came up in priority to the company goals. It was a great environment to learn a generalist skill set and understand how direct your work impacts the business.
- Very often, people decide to join startups because of the team they’ll be able to work with. What they don’t think about is how that team can change if their startup gets acquired. Acquisitions cause a lot of shuffling around, and sometimes that can mean splitting teams apart, or changing a team’s priorities considerably.
The podcast was awesome. Jeremie and Russell interview a ton of different people in data science on the podcast so I would highly encourage everyone to check it out.