🚀 Jobs metric: 152 new jobs in the last two weeks!! 🚀 

New  jobs is Sports

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 great python package by Sean O'Connor. He created a package to make it super easy to get lots of rugby data to analyze and create projects.

from rugbypy.match import *
matches = fetch_all_matches()

Jobs for you:

These were some of the uploaded in the last 5 days only!

• Sr. Data Analyst - Miami Heat

• Senior Quantitative Researcher - Risk Modeling - Swish Analytics

• Hockey Data Engineer - NHL - Edmonton Oilers

• Digital Performance Analyst (M/F) - French Football Federation

• Quantitative Researcher| Sports Analytics - SIG

• Data Analyst Intern - Milwaukee Brewers

• Data Scientist - Legend

   

8000+ players, 250+ teams, 6000+ games over the 2022, 2023, 2024 & 2025 seasons - rugbypy is a Python package that aims to make rugby data more available to aid in the development of rugby analytics.

Shortcuts:

this is the repo: https://github.com/seanyboi/rugbypy

and some extra documentation: https://seanyboi.github.io/rugbypy/

This is a shout out to Sean O’Connor for creating and sharing this repo. It’s super clean and follows some best practices on how the data is displayed, format, structure, etc so it’s super easy to start playing around.

Using a Python package like rugbypy gives you direct access to rich, structured rugby data, from match results and team stats to detailed player performance, which is awesome to get more hands-on experience with real sports datasets.

You can load and explore the data, compute meaningful performance metrics, test hypotheses and build interactive visualizations to tell stories from the numbers. It’s perfect for creating a project that demonstrates your analytical workflow: pull data programmatically, clean and transform it with tools like pandas, visualize trends with libraries like matplotlib or plotly, and even prototype custom metrics for scouting or reporting. The more you work end-to-end, the more you show your work and deepen your understanding of both Python and sports analytics, making your portfolio stand out.

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