Personality traits for a data scientist

Personality Traits for a Data Scientist; My top 3 explained.

My top 3 personality traits are probably not the standard, it has to do with how I define a great data scientist. This is a troublesome endeavour in general. Data Scientists are everything and everywhere these days and the field of data science is very broad indeed. It is however important to see these top 3 personality traits for a data scientist from the right perspective.

A great data scientist facilitates value growth within a company by making connections with others and helping people and projects forward in a data-driven way.

Let me explain this shortly, I see a data scientist as someone who drives innovation through data-driven decision making. You know all the technical stuff, the difficult bits. As a great data scientist you move beyond that. You create impact by working with people and helping them think and act differently. You end up as a bridge between the technical expertise and the way people work on a daily basis. Understanding one, and not the other, makes you destined to fail.

Noone likes failure, and I’m not gonna write this blog from a perspective of how to avoid failure. I want to talk about succes. Because in my own experience, there are some important traits to learn to cultivate within yourself, to become a great data scientist.

Personality traits 1: Horizontal curiosity

To be curious, almost childlike, should without a doubt be deeply ingrained in your system. There are many facets of curiosity, starting out in a new industry or company we need this skill to adapt to our new environments.

Luckily in such a fresh new environment curiosity comes natural to many. I am also looking for when it does not come natural. When it no longer gets triggered from outside stimuli. What happens when we dont emphasize curiosity from within? We slowly descent into set rhytms which where the only way out is a change in surroundings. This is no healthy and durable strategy, finding and maintaining curiosity from within yourself gives you longevity in your daily life.

Curiosity has another very important facet, it is not only about duration but about direction. To become a successful bridge between teams and find business value within your company. You need to be directing your curiosity horizontally.

Horizontal curiosity means broadening your field of interest, learn about people and their jobs, find parallels and bring them into your own daily routines and work. This is different from vertical curiosity, vertical curiosity is about deepening your knowledge on an ever specializing skillset. This distinction is so often overlooked and in my experience everyone talks about vertical curiosity. We need to be curious about deepening our knowledge. But as a functioning, great data scientist that brings people together to innovate towards new data-driven processes, we desperately need horizontal curiosity.

The great news is cultivating this is easy, if your new to horizontal curiosity, start slow and ask questions to people you speak about areas of their life. Eventually you will see that in every conversation you ever have, you can be horizontally curious.

Personality trait 2: Creativity

I’ve started to read this great book, “Big magic”, by Elizabeth Gilbert, its relatively old and printed in 2015, but new to me (You can find it here ). It talks about creativity,  how it is the gateway to our personal hidden deep treasures. Our life becomes so much richer if we learn to open this gateway. I’m not that far in yet, but it got me curious.

If creativity enriches your own life, which I’m pretty sure it does, I definitely will argue it enriches the lives of the people around you. And if it does that, then it enriches your company and workplace. As a data scientist we share our creativity with our coworkers by imagining new solutions and products. We are in the business of envisioning new horizons, talking to people about them and making it a reality.

“To create”, in other words, “bringing something into existence”, is an actionable skill. You need to do, talk, write, code, visualize to create. We can cultivate creativity by learning to start this one step at a time in a conversation, an essay or even a drawing of our vision. The key aspect for me about creativity is to take that step. It will soon turn into a sprint, get those creative thoughts out of your head and onto something, it will thank you for it.

“The only limit to your impact is your imagination and commitment” –  Anthony Robbins

Personality trait 3: Tenaciousness towards people

This is the final trait I want to discuss today, I know that sentence is not screaming tenaciousness, why stop here? Do I have other plans, things to go to? Actually I am committed to writing a blog with some clarity and ease of perspective, and that’s why this is my last trait for today.

Tenaciousness is seen as hardship, lonely on the trail we walk and are taught to push on. We are told there is light at the end of this tunnel. We have to commit to our ideas and walk the path to greatness. I too firmly believe in commitment, commitment to higher purpose and the people around you. If we are committed to this greater good, and getting there together, nothing can touch us. We can walk out in front of the group, as a data scientist we have too. We offer paths to a new and different future, what we need to learn is to commit not only to the path, but the people who walk that path and the end destination.

There is a place for the more traditional view of tenaciousness. We need to be able to grind out the work, once we agree on setting off down a pathway, we need the tenaciousness to deliver. You can’t lead if you don’t know how to follow goes the saying. Tenaciousness to our own words and agreements translates to other people. To deliver on this continually, is what makes people feel that you can bring them to this higher purpose.

Wrapping up

These were my top 3 personality traits for a data scientist. If you want to develop your skillset as a data scientist, focus on these 3 personality traits. It will shape the impact you create for years to come. Brought together In the right quantities these 3 traits are on my shopping list when I am looking to expand my team and hire a data scientist.

To make valuable connections we need to be curious. To move forward in new directions we need creativity. Bringing this reality to life we need commitment to our coworkers and peers.

See you in a click!


My personal backstory; A road to Data Science for a living

As the title says, today I will share my own person backstory and  how it shaped me for data science to come into my life. As every person on the planet will tell you, their story is a little bit different, and so is mine. Now lets go down memory lane.

From a young age I’ve always been competitive, into strategy games and numbers. If I’m honest it wasn’t always completely healthy, I was always looking to confirm my own uniqueness and intellect when I was very young. For example in primary school from 6th grade you could play chess in a school selection, they would select 5 people and the winner would be  table number 1, all the way down to 5 to make a team. We would then as a team of 5 play against all the other schools in the area and number 1 would play against the number 1 of the other schools. I was number 1 3 years in a row, the first time that happened in school history and in my first year I was 9 and playing against these 12 year old’s. I thought it was awesome and it just reaffirmed everything I thought of myself.

Aside from the probably mental health concerns, what I enjoyed so much about chess from an early age is the mix between smart logic and time constraints. At the time I would also play age of empires, a real-time strategy (RTS) game that has the exact same tradeoffs between time and smart logic. This was my next addiction, during high school I competitively played RTS games online, I played a lot of e-sports but this was when the entire esports scene was in its infancy. RTS games taught me 2 things, it taught me how to search for optimal strategies given limited information, and it taught me the concept of studying and improving your strategies outside of the competitive environment, training.

By the time I started my economics studies in 2008 I hit a different period in my life, it was right around when online poker became main stream. This game just perfectly hit my interests, it was a natural evolution after playing RTS games, only the stakes were far more interesting. What was new about Poker? It was more math intensive, it brought me into the world of databases and statistics. It connected to my studies that brought me game theory and behavioral economics.

It was through online poker that I first got into contact with a programming language, there was this professional player who developed his own tool to calculate game theory optimal strategies in a given situation. He did this in python and shared his notebooks with everyone who followed his course. This was a very interesting new challenge for me, by going through his code and altering it to my own needs I self-taught basic Python. Later during my master we did some Stata, which really isn’t programming, but we also had the option to use R. I taught myself R too, just by copying code, looking online and using it for my master thesis.

By the time I finished my studies in Health Economics I was ready for a real job, interestingly enough I was not directly looking for a job in data science, it was economic analysis that was my principal interest. I actually had a job offer in hand from a consultancy in Rotterdam specialist in health economic analysis. At the time of that job offer I just started working part time doing some programming work for a data scientist who worked for this insurance company, MediRisk. He told me that they are looking to expand and got me an interview there. It was my curiosity for something new that brought me to the decision to go with the data science position at MediRisk.

See it can be easy to put something on a page and make it seem like a logical buildup and story, and in a way it was, but the real red line was always my curiosity for something new. Perhaps not completely new, but always one step forward and one step sideways. I love to broaden my view of the world by learning new skills, ideally by doing them. In my job currently I get to constantly explore new tools and technologies, understand them for their potential and excite others to join me in using them to create value. Learning by doing something new is something I aspire to always continue doing.

In a next blog I will share some tips and tricks into what I feel is most important if you want to start as a data scientist.