Unlike a lot of data scientists in tech, my career in data science started in consulting, and I think it’s the best career move I have made. Say what you will about consulting culture and the hours, I learned so much in the two years I was at McKinsey and I still benefit from it every day.
As a manager, part of my job is to coach data scientists on the team when it comes to projects and career growth in general. I realized what junior data scientists struggle with the most is usually not the technical/execution part of the job — that’s the easy to teach/easy to learn part.
It’s usually the more abstract/soft-skill-related part of the job that most people don’t know how to navigate — things like how to break down an abstract business problem into smaller, clearly defined analyses that can eventually lead to concrete business impact.
These are the things I got to practice day in day out as a consultant, and I think the learnings carry over to data science very well.
To help my fellow data scientists, I want to summarize my learnings from my consulting days so you can benefit from them without going through the grind.