Lead Data Scientist

Overview

In this role you will provide technical leadership for data science, ensuring production-ready ML solutions align with business priorities. You will guide modelling and solution design across initiatives, mentor teams, and implement standards for reproducibility and deployment. You’ll collaborate with architecture, ML & Data Engineering, and BI to ensure scalable, reliable outcomes, while elevating the organization’s data science maturity. This is a high-impact position at a fast-growing company shaping its data-driven capabilities.

Pay / Benefits

• Performance-based Bonus
• Funded Healthcare benefit
• 25 days holiday + birthday and bank holidays
• Contributory Employer pension scheme
• Flexible benefits programme
• Gymshark Employee Discount

Responsibilities

• Act as technical lead for data science across multiple initiatives
• Translate priority use cases into production-ready data science solutions
• Provide hands-on leadership across the data science lifecycle (framing, modelling, deployment, optimization)
• Define and embed technical standards for experimentation, validation, and reproducibility
• Review and challenge technical designs, providing direction and sign-off
• Mentor data scientists on complex challenges
• Partner with Data Architecture, ML & Data Engineering, and BI teams to ensure scalable, production-ready solutions
• Evaluate new techniques and tools and guide their pragmatic adoption
• Communicate technical assumptions, risks, and trade-offs to technical and non-technical stakeholders
• Maintain strong stakeholder partnerships and raise the technical maturity of data science within the organization

Key requirements

• Extensive experience delivering production-grade, commercially impactful data science solutions
• Senior technical lead, reviewer, or technical sign-off authority
• Strong experience building ML solutions on cloud platforms (GCP, AWS, or Azure)
• Advanced expertise in Python and SQL
• Deep knowledge of ML techniques (regression, classification, clustering, time-series forecasting)
• Experience supporting production deployment and lifecycle management of ML models
• Strong understanding of data warehousing, data modelling, and modern data architecture
• Excellent communication skills for non-technical stakeholders

• Effective communicator
• Mentoring ability
• Strategic thinking
• GCP, AWS, or Azure cloud platforms
• Python
• SQL

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