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Demystifying Data Science: As Major League Soccer Grows, So Does the Data

By Emily Wilson • September 07, 2017

A quick Google search for “growth of Major League Soccer” yields some pretty impressive results. Multiple headlines contain phrases like, “MLS Records Banner Year in 2016,” and “Soccer Seeing Incredible Growth in the United States.” It’s a league on the rise, filled with incredible talent, currently at 22 teams with not-so-distant plans of expanding even further.Metis graduate Nelson Spencer is a Senior Analyst of Data Strategy & Analytics at Major League Soccer’s headquarters in New York City. He’s a soccer fan who grew up playing the sport, and he still plays to this day. Even so, he says the job’s main perk is the data itself, which grows in volume with the success of the league.“The most exciting part of working at MLS is the data that drives the business,” he said. “I generally spend a lot of my time thinking about how to improve overall processes, specifically how to best make data readily available and easily accessible for various stakeholders throughout the enterprise.”That’s no small feat, certainly, but according to Spencer, it’s just a building block. He enjoys what he’s doing now, placing a solid data foundation, but he looks forward to the types of in-depth analysis he and his team will be able to do once others within the organization start to get their hands on this data on a regular basis.“When people realize its accessibility and its power, we can begin to explore, ask questions, and make the data work for us to ultimately move MLS forward,” he said.Most of Spencer’s career has been rooted in his love of sports. He studied Sport Management at the University of Massachusetts Amherst as an undergrad before realizing he wanted to focus on his lifelong passion in a more specific and specialized way. He was introduced to data science around this time and felt an immediate draw, but he knew pursuing it would require further education. He did so in an unconventional way – getting a Masters in Data Science while also taking on the intensity of the Metis Data Science Bootcamp.Why both? He felt his lack of formal education in computer science and statistics required the double whammy.“I figured I’d try and get as much experience as possible. I used the bootcamp as my jump into data science. I know a lot of people use the bootcamp as their last step into the field, but I think I just came in with a unique background,” he said. “I don't think it's necessary to do both if one has a strong background in either stats or computer science, I just happened to not have either outside of a few advanced math classes in college and a couple MOOCs (massive open online courses) in Python. I'm also obsessed with learning and improving in general, so doing both was never a huge hindrance for me.”That ability to take on a variety of goals and tasks simultaneously directly benefits him in his current position, where he’s part of a small team that needs to juggle multiple projects at once. It’s both an ongoing reward and challenge to be given such a breadth of responsibility.“On any given day, I can be a data analyst, querying the database using SQL or analyzing ticketing purchase data for an upcoming event. I can be a data scientist, using Python and Natural Language Processing (NLP) to analyze social media data or build models to predict churn in season ticket holders. I can wear the hat of a data visualization engineer by building new pipelines to programmatically ingest data via APIs and create interactive dashboards to make data more accessible throughout the enterprise,” he said. “Finally, I can find myself filling the role of a project manager in terms of being able to take an idea through the iterative process of working with various stakeholders to solve it using data.”That’s quite an expansive job description, and according to Spencer, it’s not all that rare for those in data-related positions to be expected to have such a wide range of skills and abilities. He credits his aggressive, dedicated training, along with a touch of networking luck, to landing the role, but says no matter how a person gets into the data science field, adaptability should be at the forefront as they get hired and progress.“Prepare to be flexible and help out in a lot of different areas that you may not have been specifically hired to do,” he said. “This will not only make you more valuable to your organization but you’ll also enhance your own skillset. Win-win.”


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