BP - Principal Project Manager

Role Synopsis:

Responsible for leading the delivery of a segment or team investment portfolio / programme / complex high-risk project in alignment with strategy, taking full accountability for its success and using highly advanced technical capabilities to manage financial plans and realize expected benefits outlined in the business case, securing engagement and involvement of stakeholders and leading continuous improvement efforts.

Let me tell you about the role:

A Principal Project Manager at the company operates at a strategic and enterprise level, driving effective delivery across programmes, platforms, and capability initiatives. This role helps advance data-driven decision making across the business field.

The successful candidate will:

• Embed data-informed thinking into how delivery decisions are made
• Strengthen the use of statistical inference, experimentation, and evidence-based insights across teams
• Coach and support project, product, and delivery practitioners to adopt meticulous, data-driven approaches

What you will deliver:

Enterprise delivery orchestration:

Coordinate across teams, products, and regions to support impactful execution, with a strong emphasis on data-backed prioritisation and decision-making.

Data-led decision enablement:

• Establish and embed data-driven decision-making practices across programmes and delivery teams
• Support the adoption of statistical methods (e.g., hypothesis testing, confidence intervals, causal inference) in business and delivery insights
• Ensure key delivery decisions are supported by robust quantitative evidence and clearly stated assumptions
• Partner with Data, Analytics, and Product teams to improve the quality, accessibility, and usability of data

Practitioner capability & coaching:

• Lead Communities of Practice and support practitioners in:
• 1. Translating data into actionable insights
• 2. Structuring problem statements using analytical frameworks
• 3. Applying data science techniques to delivery, forecasting, and risk management
• 4. Strengthening data-backed storytelling and recommendations

Insightful risk and performance management:

• Apply predictive and diagnostic analytics to identify risks and delivery bottlenecks early
• Use data to move from reactive reporting to forward-looking performance management

Stakeholder engagement and influence:

Build trusted relationships by bringing analytical thinking and statistical difficulty to decision-making discussions, influencing interested parties through evidence-based insights.

Continuous improvement and transformation:

• Drive continuous improvement through:
• 1. Measurement frameworks with clear KPIs and statistical validation
• 2. Experimentation approaches (A/B testing, pilots, controlled rollouts)
• 3. Embedding a test-and-learn approach