What Being a Full-Stack Data Scientist at a Startup Is Like | by Ani Madurkar | Aug, 2023


Constant acceleration, highly collaborative, and always learning

Midnight sunset in Iceland. Image by author

For the last ~12 months I’ve been working as a Data Scientist at a startup in the health-tech space. I joined as one of two Data Scientists on the team, which essentially meant we needed to be full-stack engineers and scientists to get the work done and build scalable systems that set the company up for success in the future.

In this time, I have learned more than I have in any other role that I’ve held and this piece showcases three main ideologies that summarize my experience best so far.

Essentially, you need to be a paradox. You have to walk the fine line between worlds that are often at odds with one another. This aspect of the job can be really difficult to shine in, as it often involves you to be a lot more than most other roles ask of you. But for those hungry for an intensely rewarding learning experience, it’s unbeatable.

How much you have to build really depends on how early the startup is and how many people are on the Data Science/ML, Data Engineering, and Data Analysis teams. Regardless, it’ll likely come down on you to not only operate the full ML stack (data ingestion to deployment) but also to build a platform to make future projects better.

Often times this looks like being in meetings with key business stakeholders and creating models that directly impact the bottom line. This is commonly done with most data projects, but there often can be a lot more noise in this type of scenario. By definition, you’re working in a space that is trying to do something novel or solve a problem in a way that provides greater value to your customers than your competitors. This means you’re likely not going to work on traditional ML projects doing what everyone’s already doing and you’re definitely not going to get the project handed to you; you’ll be in charge of attempting to build something new. Whether it’s a novel approach to dataset curation, feature engineering, modeling, application of models, or all of the above — you should be trying to innovate (while still remaining ethical and within legal constraints, obviously).



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