Speaker Spotlight – Jerry Mundondo

My name is Jerry and I have been working in data for about four years now. I started out my career in Sports Science before pivoting full-time into data via a conversion masters at the University of Liverpool. I am now lucky enough to have been working in sports data for coming up on two years, allowing me to get lost in my two passions (sports and data) every single day.

How did you start out in your tech career?

My career in data started out with a conversion master’s in Data Science and Artificial Intelligence from the University of Liverpool. It was a one-year programme with the option of a second year, where the second year would consist of a year-in-industry. I opted for a year in industry, meaning my first job was as a data science intern at a games marketing company during the year in industry. I learnt a lot of skills that could not be taught in the classroom, allowing me to grow as a data scientist. But in terms of tangible technical skills, the games marketing company I worked at was a start-up, meaning employees were expected to be highly flexible. As a result, my role was not that of a pure data scientist but a full-stack data professional, doing a fair amount of data engineering and data analysis.

Following on from this first job, I took on a job as a data engineer in sports, beginning my transition to being a sports-specific data scientist, culminating in my current role at Statsports.

What is the best and worst thing about your job role?

The worst thing – data collection. Data scarcity in any domain is nothing new, but in the sports sector where data can only be collected by athletes physically going out to play their sport, it can make getting good data extremely difficult.

The best thing is that it is a field where I still feel data is underutilised despite the headlines suggesting otherwise. Compared to traditional industries that use data (e.g. banking, insurance, marketing), I feel that sports has a long way to go, meaning it is an exciting and growing field to be in.

What can you advise someone just starting out to be successful?

View yourself as a problem solver who just happens to use data and code as their tools.

Regardless of whatever domain you work in, this is a useful lens for any professional to view themselves through. Just like how a marketer solves the problem of product awareness using words and content as their medium, we do the same with maths and code. So find out what the biggest problems that your company faces are and assess how your skillset can help solve these problems. This will help prevent you from putting yourself in a pigeonhole and only thinking of yourself as a code optimiser, and instead become someone truly valuable to your company.

What advice would you give your younger self?

Always look to expand your skillset. When I began, I viewed myself primarily as a data scientist, but it is important to be well-rounded as a data professional. That means also being a more than competent data analyst as well as data engineer. It will not only make you more valuable to your employer but also easier to work with for colleagues as you understand their roles as data analysts/engineers.

Secondly, try to expand your skillset outside of direct coding/mathematical skills. This could be through taking an interest in becoming a better presenter, levelling up your knowledge in marketing, or any field relevant to your company’s goals. Speaking the language of data is great, but speaking the language of business is just as important.

What is next for you?

My goal is to continue to grow and develop as a sports-specific data scientist and become well-renowned for the quality of my work in this specific subdomain.

What are your top 5 predictions in tech for the next 5 years?

Specifically in the sports data space:

• The use of data will continue to explode as at present I feel like we are barely scratching the surface

• Data use becomes more mainstream in individual sports such as tennis rather than the traditional big-money sports

• Real-time analytics will become standard at amateur and youth levels, not just professional sports, as sensor technology becomes more affordable

• AI-powered injury prediction models will become sophisticated enough that they’ll fundamentally change how athletes train and recover

• The gap between teams that effectively use data and those that don’t will widen dramatically, making data literacy as important as traditional scouting for sporting success

Watch Jerry’s session at the Data Science Festival here.

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