Principal Data Scientist

Role overview

The Principal Data Scientist will lead significant data science work across AWTG and client programmes, shaping technical direction, standards and delivery approaches. The role requires deep expertise in data science, strong leadership and the ability to turn advanced analytics into organisational impact.

You will work with multidisciplinary teams across data, AI, software engineering, product, QA and delivery to create practical outcomes for clients and end users.

Key responsibilities

• Lead the scoping, design and delivery of complex data science products, models and analytical solutions.
• Set standards for modelling, validation, reproducibility, coding, documentation and responsible AI practices.
• Identify opportunities to apply machine learning, predictive analytics, optimisation or advanced statistics to business challenges.
• Guide data scientists, analysts and engineers in selecting appropriate methods, architectures and tools.
• Influence stakeholders by communicating technical options, risks, benefits and evidence-based recommendations.
• Support capability building, mentoring and continuous improvement of the data science practice.

Essential skills and experience

• Extensive experience designing and delivering advanced data science solutions at scale.
• Deep understanding of applied statistics, machine learning, model validation and data science product delivery.
• Strong programming capability and ability to set coding, testing and reproducibility standards.
• Experience working with data engineering, cloud, architecture and software teams to deliver supported solutions.
• Strong stakeholder communication skills, including challenging delivery plans and priorities when required.
• Strong understanding of data ethics, privacy, bias, explainability and governance.

Desirable skills and experience

• Experience with MLOps, AI platforms, NLP/LLMs, knowledge graphs or real-time data products.
• Experience setting data science strategy or leading cross-functional technical communities.
• Experience in consulting, public-sector, regulated or high-complexity client environments.

What success looks like

• Complex data science work is delivered with clear standards and measurable business impact.
• Technical choices are well governed, scalable and aligned to delivery realities.
• Data science capability grows through mentoring, reusable patterns and shared practice.