Hiring nowService, hospitality, sport and accessibility AI

Sports AI Analyst

Also called Sports ML Analyst

A Sports AI Analyst turns performance data and models into analysis that coaches and practitioners can act on. They matter because a prediction nobody trusts, or that ignores consent and context, is worse than none.

What they do

The analyst takes tracking, event, wearable or video-derived data and works out what it can honestly say about performance. They check that models are valid for the sport, the level and the athletes involved. They agree with practitioners what question is being asked, then build analysis and decision-support output to answer it. They test predictions against what coaches see in practice. They check that consent covers how athlete data is used, and who can see the results. They write up limits plainly, flag where a model should not be trusted, and revise outputs after feedback from the people using them.

What a strong hire delivers

In the first months, a strong hire produces a clear picture of which current analyses and models hold up and which do not. They deliver decision-support output that authorised practitioners actually open and use. They set up a repeatable validation routine for new models. They put consent and access rules in writing, and they give the team a shared view of what the product can and cannot claim.

How you can tell they are good

Ask for one analysis they built and what a practitioner did with it. Good candidates explain how they tested validity, not just accuracy, and name the context that changed the result. They can describe a time they told a coach a model was not good enough. Be wary of anyone who talks only about metrics and never about the people using them.

Why it is hard to fill

Few people sit between sports analytics and applied AI, and job titles vary widely. Many strong sport analysts have little model evaluation experience, while many ML people lack a feel for sport. Practitioner credibility matters as much as technical skill. Clubs and federations rarely advertise, so good candidates are often not visibly looking. Mixed backgrounds are normal here, and that makes screening harder.

Where to find them

Look at performance analysts and data scientists in professional clubs, national governing bodies and institutes of sport. Sports technology companies, wearable and tracking providers, and broadcast analytics teams are also good sources. Some come from academic sport science or biomechanics. Others come from adjacent service and product roles, such as user research or accessible design, and have moved towards sport.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Sports AI Analyst

  1. Tell me about a sports analysis you built that a practitioner used to change a decision. What happened?

    A strong answer names the practitioner, the decision and the outcome, and admits what was uncertain. Look for evidence of real use. Worry if the answer stops at a dashboard or a model score with no one acting on it.

  2. How would you test whether a prediction model is valid for a new squad or competition level?

    Good answers cover distribution differences, sample size, validation on held-out seasons or teams, and practitioner review. They question transfer between contexts. Worry if the answer is just overall accuracy or a train and test split.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We build AI products that help sports practitioners understand performance and make better decisions. We are looking for a Sports AI Analyst to turn data and models into analysis that is valid, used and respectful of athlete consent. You will work closely with coaches, analysts and our product team.

What you will do

  • Analyse performance data with the context of sport, level and athlete
  • Evaluate and validate models before they reach practitioners
  • Produce decision-support output that authorised users can act on
  • Gather practitioner feedback and revise analysis and outputs
  • Check that data use matches athlete consent and access rules
  • Document model limits and where outputs should not be trusted

What you will bring

  • Demonstrated sports analytics work used by practitioners
  • Experience evaluating models beyond headline accuracy
  • Sound interpretation of sport-specific performance data
  • Clear written and spoken communication with non-technical staff
  • Practical understanding of consent and data access in sport
  • Comfort working with analytics tools and user testing

Nice to have

  • Experience in a club, federation or institute of sport
  • Background in accessible or service design
  • Experience with wearable or tracking data

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Service and conversation designAdvancedStructure dialogue, escalation and service experiences
  • Human-computer interactionAdvancedDesign and study interactions between people and AI
  • Model evaluationWorkingMeasure capability and failure modes credibly
  • AccessibilityWorkingEnable equitable use by people with disabilities

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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