Data Scientist

About The Role

Role Purpose The Data Scientist will be responsible for developing and maintaining statistical and machine learning models that generate insights, predictions, and decision support. This role focuses on extracting value from data through rigorous analysis, modelling, and experimentation, while clearly communicating results to stakeholders throughout the club. The role owns models throughout their lifecycle, from development and validation to monitoring and improvement.

Main areas of responsibility

• Explore, clean, and prepare datasets for statistical and machine learning modelling.
• Develop, validate, and maintain quantitative models using appropriate techniques and libraries.
• Perform feature engineering, model selections, and hyperparameter tuning.
• Evaluate model performance using suitable metrics and validation strategies.
• Monitor models over time to detect performance degradation, drift, or bias.
• Recommend and implement model improvements as new data or requirements emerge.
• Produce clear documentation, visualisations, and presentations explaining model outputs and implications.
• Translate football specific questions into well-defined modelling solutions.

General Responsibilities

• BWFC seeks to ensure that all children and young people are protected and kept safe from harm while they are with staff and volunteers within Bolton Wanderers activities. Everyone at Bolton Wanderers has a safeguarding responsibility to all work colleagues, fans and any vulnerable adults and children.
• Comply fully with all data protection and confidentiality obligations, ensuring that personal, sensitive, and confidential information is handled lawfully, securely, and in accordance with UK GDPR and the Club's Data Protection and Confidentiality Policies.
• Be an ambassador of the Group, providing excellent customer service at all times whilst portraying a professional image.
• Perform other duties as required, which are considered relevant to the post and to the objectives of the Group as identified by the Lead Recruitment & Data Analyst.
• Treat all colleagues as customers ensuring respectful positive outcomes across communications.
• Adhere to and abide by all BWFC policies, procedures, and guidelines especially remembering responsibilities to others under Equal Opportunities, Health and Safety, Equality and Diversity.
• This post is subject to a DBS disclosure.

Essential Requirements

Qualifications

• Bachelors degree in Computer Science, Physics, Mathematics, Statistics, Engineering, or a related quantitative discipline (or equivalent practical experience)

Experience

• Strong experience in Python.
• Experience writing SQL to extract, join, and manipulate data from relational databases or data warehouses.
• Experience preparing datasets for modelling, including data cleaning, feature engineering, and validation.
• Proven experience building and deploying statistical and machine learning models using Python libraries such as Sci Py, scikit-learn, Tensor Flow (or similar deep learning frameworks)
• Strong understanding of:
  Probability and statistics, supervised and unsupervised learning techniques & model evaluation, validation, and selection.
• Experience selecting appropriate modelling approaches based on data characteristics and objectives.
• Ability to evaluate, monitor and diagnose model performance over time.

Knowledge, Skills and Qualities

• Ability to prepare and present clear documentation, visualisations, and insights for stakeholders.
• Comfortable explaining complex modelling concepts and results to non-technical audiences.
• Excellent communication skills.
• Resilience and problem-solving attitude.
• Strong organisational and planning skills.
• Ability to effectively prioritise work.
• Attention to detail.

Desirable Requirements

Qualifications

• Masters degree in Computer Science, Data Science, Statistics, or a related field.

Experience

• Experience with web scraping and ingesting data from external sources.
• Familiarity with data architecture concepts, including data pipelines and data storage design.
• Experience with Snowflake or similar cloud-based data warehouses.
• Understanding of applied modelling challenges in sports performance, recruitment, or…