First Team Football Data Scientist – 26 Days Leave

PFSA Education Ltd.; Manchester; Hybrid; GBP 60,000 - 90,000; Full time

Benefits offered by this job

26 days' annual leave

Birthday off

Private healthcare

Dental cover

Discretionary bonus

Pension

Job summary

Manchester City is seeking a First Team Data Scientist in its 1st Team Analysis department. You will build models and insight that inform recruitment, training and match strategy for Manchester City and the wider City Football Group.

Role responsibilities include researching data science solutions, developing statistical and machine-learning models, exploring patterns, testing ideas, and presenting findings to both technical and non-technical audiences across teams.

Qualifications

• Master's degree in maths, statistics, physics, computer science or related subject.
• Experience designing and training complex statistical models.
• Strong Python skills for analysis and model development.
• Experience with structured and unstructured data, including spatio-temporal data.

Responsibilities

• Research and build data science solutions for performance and recruitment questions.
• Develop statistical and machine-learning models across technical, tactical and physical areas of the game.
• Explore patterns, identify useful features, test ideas and make predictions to inform training and match plans.
• Present findings to technical and non-technical audiences.
• Work with data scientists, analysts and other departments on shared projects.

Skills

Python

Statistics

Machine Learning

Data Modeling

Spatio-temporal data

Education

Master's degree in maths/statistics/CS

Tools

SQL

Pandas

Scikit-learn

Job description

Manchester City is seeking a First Team Data Scientist in its 1st Team Analysis department. You will build models and insight that inform recruitment, training and match strategy for Manchester City and the wider City Football Group.

Role responsibilities include researching data science solutions, developing statistical and machine-learning models, exploring patterns, testing ideas, and presenting findings to both technical and non-technical audiences across teams.