Data Scientist

Contract Type: Permanent

Hours: Full Time, 40 Hours Per Week

Location: Bramall Lane

Department: Data

Line Manager: Data Manager

Salary: Starting at £40,000 per annum

Post Reference: BL012026-PDS

Sheffield United Football Club is seeking a Data Scientist to join its centralised data team, playing a key role in transforming complex data into actionable insights that inform strategic and operational decision‑making across the Club.

This is an exciting opportunity to work in a collaborative, fast‑paced environment where data science has a direct impact on business performance, commercial success, and long‑term planning.

As a Data Scientist, you will apply advanced analytics, data modelling, and machine learning techniques to solve real business problems across the Club. Working closely with stakeholders in finance, retail, commercial, and marketing, you will translate complex questions into robust, data‑driven solutions that deliver measurable impact.

You will design, build, and maintain scalable analytical solutions using modern cloud‑based platforms, communicate insights clearly to both technical and non‑technical audiences, and play an active role in embedding a strong, data‑driven culture throughout the Club.

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• Analyse and model commercial, marketing, ticketing, and retail data to generate actionable insights that inform decision‑making across key business functions.
  
• Develop and apply machine learning models to predict fan behaviour, supporting optimisation of commercial strategy, marketing activity and retail performance. Apply forecasting techniques to support future planning and decision making.
  
• Design, build, and maintain high‑quality dashboards and visual analytics that make complex data accessible and meaningful for key stakeholders.
  
• Communicate analytical findings and model outputs clearly to both technical and non‑technical audiences, influencing strategic planning and operational decisions.
  
• Collaborate closely with colleagues responsible for other domain areas, contributing to shared analytical initiatives and maintaining sufficient domain knowledge to provide support and continuity when required.
  
• Any other reasonable requests as required by management.
  

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• Adhere to all Sheffield United Football Club's Safeguarding Policies and Procedures to foster an environment which protects from harm those defined as children and adults at risk.
  
• Report any concerns of a Safeguarding nature to the relevant parties and remain fully compliant with any applicable Safeguarding checks and due diligence and recognise your responsibility to the Club's Safeguarding agenda.
  
• Report any concerns of discrimination to the relevant parties and promote a welcoming and inclusive club environment for all.
  
• Adhere to the Club's Equality, Diversity and Inclusion policies, supporting the Club to create an environment which is inclusive and all‑encompassing.
  

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• Degree in Mathematics, Statistics, Computer Science, or a related field - or equivalent industry experience demonstrating strong analytical and computational skills.
  
• Strong experience with Python (pandas, NumPy, scikit‑learn).
  
• Hands‑on knowledge of machine learning techniques, including supervised, unsupervised, and semi‑supervised learning.
  
• Proficiency in SQL for data extraction and transformation.
  
• Experience building clear and engaging data visualisations using tools such as Power BI or Tableau.
  
• Proven experience in developing data‑driven solutions in a finance, retail or marketing role.
  

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• Experience using version control systems such as Git within collaborative data or analytics teams.
  
• Strong ability to communicate data‑driven insights clearly and persuasively to non‑technical stakeholders.
  
• Previous experience applying consumer behaviour insights to support data‑driven decision‑making.
  
• Knowledge of core data modelling and data architecture principles within analytical platforms.
  
• Familiarity with MLOps tools and workflows, including model deployment, monitoring, and versioning.
  

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• Stage 1: Getting to know you and competency based questions.
  
• Stage 2: Presentation Task.