Meet two “mathemagical” data scientists: Jos and Joline

The Data Scientist. Often described as the hottest job of the future. But what does a Data Scientist really do? What is his/her value for a company? And is their job really that hot? Why not ask Joline Jammaers and Jos Polfliet, two Data Scientists at SAS Institute?

Why do organizations need Data Scientists?

Joline: “Today more data are generated and captured than ever before. The type of data is more diverse and companies don’t always leverage them to the fullest yet. This is where our job as a data scientist plays a crucial part.”

What does a Data Scientist do?

Jos: “In the ‘mathemagical' fairytale of my statistics and data mining courses, there was a perfectly ready dataset and a clear research question. Analyzing the data involved thinking about mathematical structures, trying to visualize 10-dimensional regression surfaces and rigorously checking all conditions and possible sources of bias.  Life was beautiful. But just like Neo awoke from his nirvana from the Matrix, starting to work as a data scientist was quite a shock at first but proved to be an awakening later. In the real world, data is often messy, incomplete and in a ridiculously hard format. What I learned at the university was how to be a data analyst, not a data scientist. Reading in and preparing the data requires a creative and technical approach in which a lot of programming is involved. Clearly defining what has to be analyzed or predicted is often an iterative process with the customer at hand. We try to understand their business problems and figure out a way to translate that into hard queries and solid numbers.  This requires both business insights, communication skills and a broad knowledge of different statistical techniques and approaches. Reporting a p-value in a business context is boring and unimaginative. We data scientists approach reporting as an artistic form of expression. Visualizations should be aesthetically pleasing and captivating, while having a high information density. Often, we find surprising connections and patterns that were previously unknown.”

What does working as a Data Scientist at SAS entail?

Joline: At SAS, we have a dozen of data scientists who are top notch analytical gurus in their domain. It’s not uncommon for us to sit together and brainstorm on how we could get the best results of our insights we gathered with the data of our customers. It’s really a team effort to come up with the best innovative ideas for analysis. Once we find the model that shows the best results, we go back to our stakeholders and explain what the model tells us. Nobody wants to rely upon a model that they don’t understand, so we make sure that our statistical techniques are translated into business language. This is, for us, the point at which we’ll understand if our model is going to fly or die. After this stage the model needs to be implemented. This means that the model is embedded in the business processes. Depending on the project, it will be used behind the scenes to make real time decisions on the actions that are taken or used as strategic insights. It doesn’t stop there, the results of the models are carefully monitored throughout time to make sure the performance is guaranteed, if not we’ll have to find out why the performance is dropping and how we can solve this.

Jos: “I agree with Joline: teamwork is crucial. What I also really love about working as a consultant at SAS Institute, is that it really drives us to the forefront of technology as well as pushing the limits of our own capabilities.”

Is it really that much fun, being a Data Scientist?

Jos: “Being a data scientist is wicked fun. Because it is such a multidisciplinary job, there is no room for boredom and routine. Every problem is something completely new, requiring an innovative way to look at it by combining existing knowledge with expert input and creative ideas.

Joline: “Our project lead times are quite short (< 6 months) because we like to focus on quick wins and make sure that the time to results is limited. This is very motivational: as a data scientist you’re eager to have a quick feedback loop, to know that your customer is satisfied and to be assured that your work is really generating business. Additionally, you know that the industry you’ll be working in six months from now, can be completely different than today. We will never be short of new challenges, and that’s the way we like it!“