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Smart Collaboration, Dream Teams and Agile Data Science Smart Collaboration, Dream Teams and Agile Data Science

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To help you understand the scale of transformation data scientists are driving at Johnson & Johnson today, Michael Phelan, Ph.D. invokes Thomas Edison's invention of the electric light bulb more than a hundred year ago. While the light bulb literally dazzled the public, Michael explained, the network of innovation behind it was equally impressive.

"The real story," Michael said, borrowing an argument from one of his favorite books, "is that Edison also had to create this reliable source of electricity, a system or grid for distributing it, a way to connect individual light bulbs to a grid, meters to gauge usage and so much more."

Innovating at scale, bridging gaps and building networks of innovation are also central to Michael's work here. Learn how he's bringing great ideas to life—and why your next "light-bulb moment" could help us change the future of health.

Creating Networks of Innovation

Among the many innovative data science projects Michael has contributed to at Johnson & Johnson is the Data Science Customer Studio, which he presented at the inaugural Data Science Showcase.

What is it, exactly? Essentially, it's a platform that helps our teams collaborate with our customers and take agile approaches to data science problems—which, Michael will be the first to admit, is something of a controversial approach.

"Some people will tell you that agile methodologies can't be applied to data science," he said. "I disagree."

That's because, in Michael's view, data science is an iterative process of transforming data to answer a question in a logical order. So, while he acknowledges that certain challenges exist ("Data science is not a linear process," for example), he also believes it's the best way for business partners and data scientists to align on key questions—and iteratively drive results from there. Think of it as a corrective lens, something that sharpens everyone's vision.

"Arguably the most difficult phase of a data science project is articulating the business question that has a discreet outcome," Michael explained. "But using an agile approach, if we discover we don't have the right data, for example, then we can come back to our business partners and say, 'This is the data you would need to answer that type of question.'"

The project also highlights the ways that we embrace outside-of-the-box thinking, since we know it's what makes "light-bulb moment" possible. It was fitting, then, that Michael presented his findings at our world headquarters in New Brunswick, New Jersey—about ten miles from Thomas Edison's famous research center at Menlo Park.

Introducing Open Learning Courses

Michael foresees a near-term future in which data science applications like the Data Science Customer Studio are embedded in the day-to-day work of nearly all of his colleagues at Johnson & Johnson. "Data science fluency is crucial to everyone's career," he said.

So, he's committed to making upskilling, training and development opportunities available to everyone, since that's the only way we'll bring that vision to life.

To that end, as part of the Data Science Customer Studio, Michael partnered with Joann Imrich to put together an interactive training course. Covering "tips, tricks, and myth busters,” the instructor-led, five-week course took a practical approach to upskilling teams on agile supply chain data science and culminated with an internal certification.

And with more than 150 participants, the course proved to be a big hit. Joan and Michael went on to present their findings at our inaugural Data Science Showcase.

You don’t have to spend a fortune and study for years to start working with big data, analytics and artificial intelligence. Practically anyone can learn the fundamental skills needed for data science—and there are tons of resources here to help you do just that.

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Even for seasoned data scientists like Michael and Joann, however, continuous learning is a key part of the job—in part because failure, experimentation and learning so often go together.

“Data science requires an exploration and discovery mindset,” Joann explained. “When things don’t pan out, you have to learn from it and keep moving forward with grit and grace. Dare to be bold!”

Michael agreed: "Failure in data science is good. Failure is welcome. It means you aren't happy with the model you have. As a data scientist, it drives you to do more and achieve better results."

Join Johnson & Johnson

Looking to take on complex challenges while being challenged to grow and making a positive real-world impact? Michael has one piece of advice: "Consider going into supply chain—you'll find such a rich smorgasbord of problems, risks and opportunities there."

Check out supply chain roles we're hiring for right now, as well as all of the openings we have in data science. Plus, if you want to stay in touch and get updates about jobs that might interest you in the future, be sure to sign up for our Global Talent Hub.

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