https://ift.tt/KAVxdj5 DataRobot is home to a treasure trove of Data Scientists. We’ve started a series called “Data Scientist Spotlight” ...
DataRobot is home to a treasure trove of Data Scientists. We’ve started a series called “Data Scientist Spotlight” as a way to meet the people behind the technology and introduce you to our great team.
Introducing Alex Tsourmas. Alex works as a Customer-Facing Data Scientist within DataRobot’s Go-To-Market team, as the company continues to build its premier AI Cloud platform for organizations across the globe.
Why do you think data science is such an in-demand job right now?
Data science itself has come to the forefront due to a confluence of factors—the prevalence of data, the rise of relevant open-source libraries, and the general interest in the AI movement. In some sense, AI and data science represent one of the forefronts of computing more generally.
In terms of being in-demand specifically within the job market, it has been a good long while since companies initially realized the dire need for talented data scientists to drive change within their businesses. Furthermore, many of these companies have matured beyond the experimentation phase and are looking to realize value from their investments in data science programs.
Education programs have rightfully begun to address this massive shortage in supply, and we’re seeing huge numbers of graduates from programs that didn’t even exist five or ten years ago. But there is still a shortage due to this massive demand, and that is likely to continue into the near future.
What kind of impact do you think a data scientist can have on an organization?
Data scientists often find themselves writing code to automate or predict things related to important datasets on these really massive, scalable problems. This is especially true when dealing with a large organization. Because of this unique situation, data scientists can be some of the most impactful people in an entire organization. When done successfully, our work can make a huge difference in terms of revenue generation, cost savings, process improvements, or even just unique insights that can change the way an organization operates.
What is one of the best things you have learned on the job and how has it shaped your outlook or career?
I worked in a highly unstructured environment early on in my career as a data science consultant. This meant that I was hopping on planes every week, visiting all types of clients, solving various problems where I often knew very little ahead of time.
Because of this I learned a combination of resilience and flexibility that I think would have been hard to learn anywhere else had it not been for that experience early on. I’ve spent a lot of time coding in the back of Ubers and on those small airplane trays!
What skills do you wish you had learned earlier in your career?
Early on in my career, I didn’t quite have a system to keep myself organized, both personally and professionally. I’ve carefully developed one over time, and with it has come clarity and peace of mind along with efficiency in getting done what I have to do.
How can a data scientist make the most impact within an organization?
A data scientist has to ensure they always are mindful of the big picture. Data scientists are often hired to influence change at a business using a particular set of skills. Keeping that in mind—that the end goal is really to influence change, and not just to write code—can prevent a data scientist from getting too lost in any particular details.
85% of models fail to make it to production – how do you deal with failure?
Failure, if it comes from a place of good intention and effort, is acceptable and even necessary. However, I always ensure that I learn from that failure, diagnose the root causes, and take those learnings with me moving forward.
What does a successful data science program look like in the enterprise?
A good data science program looks a lot like any other high-performing organization, whether it be a sports team or something else. You need teamwork, collaboration, commitment, talented players, and a whole lot of other factors to come together to really perform at a high level.
How does DataRobot make a data scientist’s job easier?
DataRobot makes a data scientist’s job significantly easier across the entire ML lifecycle. Each one of the steps involved – from data exploration, to model building and deployment – can take weeks, if not months. DataRobot helps data scientists rapidly iterate oнn ideas and realize business value much faster than if they were trying to do the same work manually.
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