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Data science, Analytics and software jobs in sports analytics. As always, the most complete site to get all the relevant opportunities in one place!

This edition I bring you recent jobs as always but also a short note by Andrew McHutchon, Head of Data Science at McLaren Racing about how they use Machine learning to model certain aspects that are not feasible to model from first principles.

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I’m also sharing this success story I got into the mailbox. It makes me happy, proud and I encourage everyone to do the same (if Sportsjobs helps you, but also in other aspects of your life), it’s super motivating receiving this kind of messages. I hope this fuels your own drive to keep pushing forward in sports analytics

Jobs for you:

• Sr Data Scientist - St Louis Cardinals

• Data Scientist - Cleveland Guardians

• Data Engineer - sumersports

• Software Developer - NHL - Tampa Bay Lightning

• Data Scientist - NHL - Tampa Bay Lightning

• 2026 Football Information Summer Intern - Detroit Lions

• Baseball Operations & Analytics Intern - Schaumburg Boomers

• Hitting Strategist - Seattle Mariners

• Quantitative Research Intern - Detroit Lions

• Staff Data Scientist - UnderDog Fantasy

• Junior Data Engineer - Everton Football Club

• Data Scientist - Legend

Andrew McHutchon, Head of Data Science at McLaren Racing says:

It's easy to see AI as something new, but Machine Learning has been an essential part of Formula 1 for many years. A key example is the "Aero Map", which predicts the amount of downforce on the car at each position around the lap. Without this model, simulating a lap would be impossible, and without lap simulations it's very hard to win, especially in a Sprint weekend where practice is so limited.

Unlike other areas of the car, you cannot model aero purely from first principles (well you can - CFD - but that's impractical for lap sims). So a data-driven, statistical model is the solution.

How the data is collected and the architecture of the ML model will differ between teams, and is part of the development battle. But all teams will have to face the challenge of modelling complex, multi-dimensional shapes that change constantly (as the car develops and parts change from race-to-race) and where data collection is highly limited.

Of course, as we are World Champions, we obviously do it best 😁😁

Source

Did you know about Aero map? Can you think about other areas where Machine Learning could help when physics models are not feasible?

It’s also an abstraction about using in some way simpler models to have enough information to make decisions when the business (or sports here) needs it. Not only we need the math and code but also having context about why we are doing things. Why it’s needed? How will we use the information?

You go into that rabbit hole and you end up reviewing data science life cycles, deployments, architectures, etc. Great value to add to a team , and in job interviews!

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