Lead Data Scientist

Overview

As Lead Data Scientist, you will set the technical direction for high-impact, complex projects and ensure scalable, production-grade delivery. You’ll mentor cross-functional teams and shape technical standards while managing risk and fostering long-term customer relationships. Your work drives innovation at the intersection of machine learning and life sciences, translating research insights into practical, commercial outcomes. This role offers the opportunity to influence strategy and delivery across multi-disciplinary streams.

Pay / Benefits

• Unlimited Annual Leave Policy
• Private healthcare and dental
• Enhanced parental leave
• Family-Friendly Flexibility & Flexible working
• Sanctus Coaching
• Hybrid Working

Responsibilities

• Set technical direction for complex, business-critical projects balancing speed, innovation, and reliability
• Design and implement production-grade ML solutions with thorough architecture documentation
• Define project problems, create roadmaps, and oversee end-to-end delivery across cross-functional streams
• Lead scoping and feasibility studies for high-value opportunities and engagements
• Manage stakeholder relationships and align technical outcomes with long-term commercial goals
• Drive adoption of best practices, resources, and robust processes across Data Science
• Mentor and develop data scientists and teams to raise technical excellence
• Collaborate effectively with Engineering, Commercial, and Infrastructure teams
• Extend technical oversight to business unit initiatives to influence organizational success

Key requirements

• Proven project management capable of breaking down ill-defined problems into actionable workstreams
• Strong customer leadership and ability to act as trusted technical advisor
• Depth in at least one ML domain and broad technical breadth across data science
• Skilled technical leader with experience mentoring teams and rolling out tools/workflows
• Ability to manage high-risk, innovative outcomes under commercial pressure
• Cross-functional collaboration experience aligning technical strategy with multiple teams
• Experience extending technical oversight to business unit initiatives

• Customer leadership
• Mentoring and people development
• Cross-functional collaboration
• Machine learning domain expertise with breadth across data science
• Production-grade system design and implementation
• Technical risk management and architecture

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