There are many articles about the skills needed to be a data scientist vs. a data analyst but there are few that tell you the skills needed to be successful — whether it is getting an exceptional performance review, praise from management, a raise, a promotion, or all of the above. Today I’d like to share my firsthand experience as a data scientist vs. a data analyst and what I learned to become successful.

I was fortunate enough to be offered a data scientist position without any experience in data science. How I managed this is a story for another time and my point here is that I only had a vague idea of what a data scientist did before accepting the job.

I was hired for my experience building data pipelines because of my prior role as a data engineer where I had developed the predictive analytics datamart used by the data science team.

My first year as a data scientist involved building data pipelines to train machine learning models and deploy them in production. I kept a low profile and wasn’t involved in many meetings with the marketing stakeholders that were the end users of the models.

In my second year, the data science manager who was the liaison with marketing left the company. From then on I was the point person and became more involved in model development and project timeline discussions.

As I interacted more with stakeholders I realized that data science was a vague concept that people heard about but didn’t quite understand, especially senior management.

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