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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 the closing keynote of 2025 Carnegie Mellon Sports Analytics Conference by Alok Pattani (Google Cloud, ex-ESPN) where he talks about genAI in sports analytics and goes through some concrete examples where it’s being leveraged.

Jobs for you:

These were some of the uploaded in the last two days only!

• Analyst, Major League Hitting - Research & Development - Kansas City Royals

• Sr. Data Analyst - MLS - Chicago Stars FC

• 2026 Graduate Summer Intern (NY) - NFL

• Analyst, Business Intelligence - Phoenix Suns

• Lead Data Scientist - FanDuel

• Graphics Data Reporter (Remote) - The Athletic

• Analyst, Player Development - Research & Development - Kansas City Royals

• Soccer Data Scientist - Europe - Swish Analytics

This is a full 40min talk. It’s really interesting and worth watching. Below a quick summary so you know the content and you can evaluate yourself if you want to watch it!

Content

How do you go from “we have PDFs and scouting blurbs” to real, on-court and front-office impact with AI? In the CMSAC 2025 closing keynote, Alok Pattani (Google Cloud, ex-ESPN) walks through concrete ways generative AI and agents are already reshaping sports analytics workflows—from MLB scouting and Olympic PDFs to an AI basketball coach built with nothing more than smartphones and clever CV + LLM glue.

Three quick examples

• MLB scouting reports with RAG
  Use vector search + LLMs to query thousands of text-only scouting reports (e.g., “relievers with heavy fastball movement”, “players with injury concerns”) as if they were structured data.

• PDF → structured data with AI agents (USOPC)
  An agent reads semi-structured competition PDFs, proposes a schema, and extracts clean CSV/JSON—removing most of the scraping boilerplate.

• AI basketball coach with phones + CV + LLMs
  A multi-camera smartphone setup plus MediaPipe + an LLM analyzes free-throw mechanics and gives simple coaching feedback, hinting at low-cost tracking tools for many sports.

This talk is great as it shows real, concrete use cases teams, leagues, and tech companies care about right now:

• Querying internal scouting text and reports with RAG.

• Automating data extraction from PDFs into analytics-ready tables.

• Building applied AI products that combine vision, LLMs, and simple rules to give coaching-friendly outputs.

Highlights skills worth investing in if you’re targeting sports analytics roles in 2025+:

• Working with unstructured text (reports, emails, PDFs) and turning it into structured data.

• Designing agents that call tools, enforce schemas, and operate semi-autonomously.

• Integrating computer vision + LLMs for performance analysis and player development tools.

• Communicating outputs at the right level for coaches, GMs, and non-technical stakeholders

Source

What do you think? Anything you would like to explore more or that could be useful for your use cases?

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