Being a data scientist @ IBM. Get comfortable with being uncomfortable

By Brandon MacKenzie         

Brandon Mackenzie

Tell us a bit about your role at IBM and what it is like to work in data science at IBM.

I really enjoy my role as a data scientist on our worldwide technical sales team. I split my time between customer-facing work, providing enablement to our technical sellers, and evangelism. With this mix of activities there is a steady stream of new and challenging opportunities. On some days I am writing code, and on others I have Hello Kitty rocketing through my slides. On my best days, I do a little of both.  

Working in data science at IBM can mean many different things. Some people work in our Research and Development Labs building next generation technology, such as cognitive systems or declarative machine learning. Other data scientists work in client-facing roles, where they get hands-on experience with analytics problems across practically every industry and geography.

What types of technical career opportunities have you had at IBM?

I have had a great mix of learning opportunities. Every customer has unique technical challenges, and there’s a constant flow of new technologies to learn. As a member of the worldwide tech sales team, I also train our sales engineers globally through various enablement materials and programs. This enables my technical expertise to have a much larger impact on IBM and our customers. Naturally, this impact leads to additional career opportunities.

In addition to the customer-facing and enablement opportunities, I also work on product planning and roadmaps with product management, development, and research. Having visibility into product development opens the door to adjacent career paths.

How are you building your expertise and eminence while working at IBM?

There are many opportunities for building expertise and eminence at IBM. I am surrounded by a team that has published hundreds of papers and books. They are very supportive and helpful in any writing initiatives that I want to pursue, whether it be technical white papers, marketing-based papers, or contributing to books. I also regularly take part in industry conferences and sit on advisory boards.

What kind of cool projects and meaningful work have you done at IBM?

The most exciting work I’ve been involved with at IBM has been in declarative machine learning. I’ve got to work alongside the SystemML team, which is a collaboration between IBM Watson and IBM Almaden Research Center. The SystemML team is a pioneer in bringing a declarative approach to machine learning. It is the type of technology that everyone will look back on and wonder why machine learning was ever done any other way.

What advice would you give to someone thinking of entering the technology industry and more specifically, with an interest in data science?

Be hungry! The data science field is rapidly evolving, and it’s full of opportunities. To work in data science you need to be a good communicator, have statistics skills, and understand how to work with data and IT. This is a rare combination. The supply and demand of these skills will provide you with exciting opportunities, and many of these opportunities won’t be in your comfort zone. So if you want to be successful in data science, then you’ll need to get comfortable with being uncomfortable.

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