A memory that has always stuck with me during my career-to-date is when my data science hiring manager drew a quadrant on the white board during one of my hiring interviews. The quadrant represents the spectrum skills you need to become a data scientist. Every data scientist sits somewhere on this landscape of skills. I was asked to draw a dot on where I thought I sat on a spectrum of four primary DS skills. The skills were: Technical/Engineering, Theoretical/Mathematical, Commercial Aptitude and Data Storytelling.

At the time, I feel I greatly overestimated my technical ability – knowing what I know now, I would have rated myself lower on this scale. Now, however, I would say I’d be offset towards technical/engineering.

I believe this type of honest self analysis is useful to any data scientist at any stage of their career, however even more so at the early stages. This kind of self awareness of your skills set can act as a compass to direct you into what areas you need to improve on and guide towards doing work that you are most likely to succeed at. Have a go at plotting yourself on the quadrant now, and then where you would like to be in, say, 5 years from now.

The truth is employers will most likely want you to sit somewhere across multiple skills groups. What use is complex statistical analysis if you can’t extract and articulate the valuable knowledge to others in the company, be it verbally or visually?

Same goes for brilliant technical data scientists who create amazing pieces of technology that hold no commercial value? Or vice versa, a data scientist has a brilliant grasp on the intricate commercial value of a project, yet does not know where to start on delivering the project. The first years of your career will enable you to feel out what types of tasks you have the greatest aptitude for, however, some of you will already know this. And for the very rare exceptional cases, you have a high aptitude for all 4 scales of the quadrant.

The DS skills quadrant:

Technical/Engineering | Theoretical/Mathematical |

Commercial Aptitude | Data Storytelling |

One of the modern dilemmas of aspiring data scientists today is that the job description of a data scientist is ever expanding, the possible skills that could fall under the umbrella of “Data Scientist” is extremely large and growing by the day. Thus, it’s important to rank these skills by some criteria. However you choose to rank these skills, ensure that you don’t become overwhelmed by the endless number of possible tools and skills you think you require. That said there are some basic skills that are fundamental to the job.