{"resourceId":"mississippi-state-director-football-analytics-ai-posting-2026-10-06","versions":[{"version":"external-ef58eb0189f19cc10ef72f4c1942023ac085a9b447791189f2fbd7b06fd1226d","resource":{"id":"mississippi-state-director-football-analytics-ai-posting-2026-10-06","title":"Mississippi State posts a Director of Football Analytics & AI role that builds predictive scouting and portal models and must police what athlete data enters generative AI tools","organization":"Mississippi State University Football / Athletics","sector":"Football operations / scouting analytics and AI","geography":"United States (Starkville, Mississippi; NCAA Division I, SEC)","publishedAt":"October 6, 2026","publicationDate":"2026-10-06","eventDate":null,"sourceName":"Mississippi State University jobs site","sourceLabel":"Institutional job posting","sourceUrl":"https://explore.msujobs.msstate.edu/jobs/director-of-football-analytics-artificial-intelligence-main-campus-starkville-ms-mississippi-united-states","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["operating-model","data-security","governance-procurement","accessibility-workforce"],"finding":"Mississippi State's posting (opening October 6, 2026) for a Director of Football Analytics and Artificial Intelligence, reporting to the head football coach, lists duties including building and maintaining predictive models for opponent tendency, situational play-calling and personnel usage; decision-support tools for fourth-down and two-point choices; self-scout predictability analysis; and evaluation models for high school and transfer-portal prospects. The role recommends which software to buy, use or build in-house, manages all football scouting and analytical software, and serves as liaison to the MSU Athletics Data and AI Lab. Duties 15–17 require enforcing appropriate-use standards for AI tools, including what program and student-athlete data may be entered into third-party or generative AI systems; keeping analytics and AI practices within NCAA and SEC rules, including recruiting evaluation limits; and maintaining confidentiality of student-athlete information. Knowledge requirements include the ability to build, validate and maintain predictive models and explain their outputs and limitations to non-technical staff. Minimum qualification is a bachelor's degree with three years of football operations, scouting or analytics experience; salary grade 15.","sledRelevance":"Why now: not archived, and read alongside William & Mary's posting it shows AI ownership being written into athletics job descriptions in the same two weeks. It moves the archived MSU story (AI-assisted stats in communications) into football competitive operations and makes generative-AI data-entry rules a named job duty. Interpretation: sport-level AI roles that report to coaches need a link to department and campus governance.","evidence":"Primary institutional job posting. It describes intended duties and required skills; it does not report model accuracy, deployed tools or outcomes. The MSU Athletics Data and AI Lab is named but not described.","architectureImplications":"Implies a football analytics stack combining film breakdown, play-by-play and tracking data, predictive models and decision-support tools, with buy-versus-build choices and connection to a central athletics data and AI lab.","governanceImplications":"Embeds AI appropriate-use rules, NCAA/SEC compliance (including recruiting evaluation limits) and data confidentiality into a coach-reporting role. Interpretation: reporting to the head coach may put competitive pressure on the person enforcing data-entry limits, so a dotted line to compliance or the data lab matters.","securityPrivacyImplications":"Explicit control point on entering program and student-athlete data into third-party or generative AI systems. Prospect evaluation models raise questions on data sources and minors' data for high school recruits.","caveats":"Job posting only; no evidence of deployed models, accuracy or results. Model validation is listed as a skill, not a reported practice.","streamIds":["college-athletics"],"roles":{"sales":"Sales framing: an SEC football program is hiring someone to build predictive scouting and portal models and to set limits on what athlete data enters generative AI. Interpretation: the gap is less modeling talent than governance, validation and buy-versus-build advice. A bounded offer is a model-validation and AI data-use review for one football analytics function. Do not claim competitive gains, model accuracy or that the hire has occurred.","engineering":"Engineering framing: the role builds opponent-tendency, situational and prospect models and owns all scouting software. Engineers should look at data lineage from film and tracking feeds, how models are validated against held-out games, and how generative-AI use is restricted by data class. Proof of value is a backtest of tendency models on past seasons and a written data-classification rule for AI tools, not a demo.","delivery":"Delivery framing: the role reports to the head coach while also policing AI data use and NCAA/SEC compliance. Interpretation: delivery should add a compliance sign-off for recruiting models, a data-classification rule for generative tools, and a link to the athletics Data and AI Lab. Acceptance should be measured by documented validation and zero unapproved data entries, not model count. Limited to well-resourced programs."},"roleDetails":{"sales":{"customerProblem":"Football programs want predictive scouting and portal evaluation but lack rules on model validation and on what athlete data can enter generative AI tools.","stakeholders":["Head football coach","Offensive and defensive coordinators","Associate AD for football administration","Athletics compliance","MSU Athletics Data and AI Lab","Athletics IT"],"discoveryQuestions":["How are tendency and prospect models validated before use on game day?","Which data classes may enter third-party or generative AI tools?","Who signs off that recruiting models respect NCAA and SEC evaluation limits?","How do you decide to buy versus build scouting software?","How does this role connect to the athletics data lab?"],"valueHypothesis":"Validated models and clear data-use rules could make football analytics more reliable and lower compliance and privacy exposure.","potentialEngagement":"A four-week model-validation and generative-AI data-use review for one football analytics staff.","unsupportedClaims":["Improved win probability or play-calling","Measured model accuracy","Deployed AI tools","Hire completed"]},"engineering":{"fit":"Power-conference football programs with film, tracking and play-by-play data and an in-house analytics staff.","architectureIntegration":"Film breakdown and tracking data feeding predictive tendency, situational and prospect models and decision-support tools, coordinated with a central athletics data and AI lab.","prerequisites":["Clean historical play-by-play and film tags","Data-classification policy for AI tools","Compliance review of recruiting data sources","Software inventory"],"constraints":["NCAA and SEC recruiting evaluation limits","High school prospect data may involve minors","Game-week timelines limit validation","Vendor data terms vary"],"security":"Classify program and athlete data, block restricted classes from public generative AI tools, restrict model and scouting data access to named staff, and log exports.","proofOfValue":["Backtest tendency models on prior seasons","Compare prospect model rankings with staff evaluations","Audit a sample of generative-AI prompts for restricted data","Document buy-versus-build decisions"]},"delivery":{"work":"Hire the director, inventory scouting software and data, set AI data-use rules, build and validate priority models, and connect to the athletics data lab.","dependencies":["Coaching staff adoption","Compliance review","Athletics Data and AI Lab","Video and analytics vendors"],"ownership":"Director of Football Analytics and AI, reporting to the head football coach, with compliance as control owner for recruiting rules.","skillsAdoption":"Train analysts and coaches to read model outputs and limits; mentor analysts, fellows and student workers.","governanceCheckpoints":["AI data-use standard approved by compliance","Model validation report before in-game use","Season-end review of recruiting model use against NCAA/SEC limits","Annual software and vendor review"],"acceptanceCriteria":["Proposed: every model used on game day has a documented backtest","Proposed: zero restricted athlete data found in generative-AI audits","Proposed: compliance sign-off on all prospect evaluation models","Proposed: software inventory with owners maintained"],"risks":["Over-trust in unvalidated models","Athlete data leakage to third-party AI","Recruiting rule violations","Coach pressure overriding controls","Key-person dependency"]}},"retrievedAt":"2026-10-18T22:16:10-05:00","enrichedAt":"2026-10-18T22:18:50-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Creates a football AI leadership role overseeing analysts, fellows and student workers.","procurementImplications":"The role recommends whether to buy, use or develop scouting software in-house and manages all football analytics software.","operatingModelImplications":"Puts predictive modeling and AI data-use enforcement inside the football staff, linked to a central athletics data and AI lab.","sourceVerification":{"responseId":"curl-msujobs-football-ai-20261019T031610Z","openedUrl":"https://explore.msujobs.msstate.edu/jobs/director-of-football-analytics-artificial-intelligence-main-campus-starkville-ms-mississippi-united-states","referenceExcerpt":"Ensure appropriate use standards are followed for artificial intelligence tools, including what program and student-athlete data may be entered into third-party or generative AI systems.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}