What Makes a Great Scientist?

Blog image for A recipe for success – what makes a great data scientist?

Data scientists are extremely talented, bright and switched on individuals.

What is a data scientist? In general terms, a data scientist will experiment with information (increasingly this is new forms of data – streams from social media, images, location data, and so on) and identify new ways of using it. They differ from an analyst, who may use traditional analytical models to gain insight from data – a data scientist will develop those models based on the information they have available to them.

I’m constantly impressed with these guys. They are extremely talented, bright and switched on. Plus, they have a diverse mix of attributes and characteristics. And that’s the key to sustaining a high performing innovation environment.

So what makes a great data scientist? Here are some of my main ingredients:

1. Above all, you must be curious
New data, new techniques – it’s all up for grabs. Curiosity is a must in exploring new ways to solve new problems and in formulating new questions that have not been asked before.

2.Creativity is key
In text books and lectures, statistics examples are clear-cut and sanitised. In the business environment, this is not the case. We are working with new forms of data, which definitely don’t conform to rules learnt in the classroom. So a good data scientist needs to be creative in thinking about and analysing very complex and often never seen before data-sets.

3. Be adaptable you never know what’s ahead
Much of what a data scientist is required to consider is coming over the horizon at pace. In all likelihood what worked yesterday will not work tomorrow. They need to make the best decisions and be adaptable in the face of uncertainty.

4. Good data intuition is a must
We live in an era of constant data generation, and much of what a data scientist will look at will be new. Good data intuition here is key – how do we know what looks right, if we haven’t seen it before?

5. Know when to stop
Much of the work undertaken in data science evolves. This could be due to new data sources or new technology for example. Being able to respond to this evolution via an iterative process of development provides us with solutions that are cutting edge. But it’s important to know when to stop! Otherwise a project can become a life’s work, and the business value is lost.

6. And finally, a great data scientist has to be comfortable with failure
We work to a success rate of 30%. Any higher and we’re not being innovative enough. But that means that seven times out of ten, we don’t succeed. You need to be comfortable with that – fail fast and move on!

*Great statistical and analytical knowledge, business acumen, and strong communication skills are a given!

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