1. Purpose of the Role

As Expert Lead – Process Excellence in SDS, you will own the process excellence pillar of the SDS transformation agenda. You will redesign end-to-end processes, simplify ways of working, reduce operational friction and create standardized process patterns that enable SDS to scale. You will work closely with SDS Transformation, AI, Governance and Service Leads, while maintaining a clear focus on process architecture, process performance and continuous improvement. This role is ideal for a senior process leader who can turn strategic ambition into practical, measurable operating-model improvements.

The Expert Lead – Process Excellence is responsible for identifying, redesigning and continuously improving end-to-end SDS processes to increase scalability, productivity, quality and control effectiveness. The role drives the Process Excellence pillar of the SDS Transformation agenda by simplifying ways of working, reducing process friction, standardizing execution and unlocking operational capacity across SDS service domains.

This is a specialist process leadership role. The role partners closely with Transformation, AI, Governance and Service Leads, while remaining accountable for process design excellence, process standards and operational optimization.

2. Operating Context

• SDS is moving toward a more scalable, product-led data service model with stronger consistency across countries, domains and service lines.
• SDS demand is expected to grow through blueprint-driven work redistribution while headcount growth remains constrained.
• The Process Excellence pillar exists to redesign end-to-end processes, unlock capacity, reduce waste and support stronger execution discipline.
• The broader SDS strategy, AI agenda, AI governance, innovation system and transformation portfolio remain with SDS Transformation & AI.

3. Key Accountabilities

End-to-End Process Design

• Analyse current-state SDS processes across priority domains and services.
• Map handovers, dependencies, rework loops, evidence requirements and control points.
• Design future-state process models that simplify execution and support global scalability.
• Translate strategic ambition into practical process blueprints and implementation-ready designs.

Process Standardization and Architecture

• Define SDS process design standards and common ways of working.
• Create repeatable process patterns that can be deployed across countries, domains and service lines.
• Reduce local deviations where these create avoidable complexity, quality issues or operational friction.
• Maintain a coherent process architecture for priority SDS services.

Capacity and Productivity Improvement

• Identify bottlenecks, duplication, manual effort, excessive handovers and non-value-adding activities.
• Drive measurable improvements in throughput, cycle time and first-time-right quality.
• Translate process improvement opportunities into concrete capacity-unlock initiatives.
• Support SDS ability to absorb increasing demand without proportional headcount growth.

Continuous Improvement Framework

• Establish a practical SDS continuous-improvement method, cadence and toolkit.
• Define process KPIs and performance-review routines with service owners.
• Facilitate process-improvement workshops and implementation sessions.
• Coach teams on process ownership, problem solving and operational discipline.

Automation and AI Readiness – Supporting Role

• Make process designs sufficiently standardized, evidence-based and measurable to enable automation or AI augmentation where appropriate.
• Provide process input to AI and automation teams, without owning AI solutions or AI strategy.
• Ensure automation opportunities are grounded in simplified and controlled process design rather than technology-first experimentation.

4. Scope Boundaries

Owns

Process architecture

Process standards

Process redesign priorities

Process performance framework

Continuous improvement method

Co-owns / Supports

Operating model design input

Service-owner implementation

AI/automation readiness input

Control-point clarity in process design

Adoption and change support

5. Key Deliverables

• SDS process-excellence framework and practical methodology.
• Current-state and future-state process maps for priority SDS domains.
• Process redesign roadmap and prioritized improvement backlog.
• Standard process blueprints and reusable process-design templates.
• Process KPI model covering throughput, cycle time, rework, quality and controls.
• Workshop materials, adoption guidance and implementation playbooks for service owners.
• Automation-readiness assessment inputs for processes selected for AI or tooling support.

6. Success Measures

Outcome area - Indicative measures

Efficiency - 20–30% improvement in throughput time for selected priority processes, where baseline and measurement scope are agreed.

Service quality - ≥30% cycle-time reduction in priority redesigned workflows, where baselines are available.

Risk and control - Improved first-time-right quality and clearer process control points.

Standardization - Adoption of common process blueprints across selected countries, domains or service lines.

Capacity unlock - Documented reduction of avoidable handovers, rework and manual effort.

Sustainability - Embedded process ownership, performance routines and continuous-improvement cadence.

7. Required Experience and Capabilities

Experience

• Extensive experience in Process Excellence, Lean, Operational Excellence, process architecture or transformation delivery.
• Proven experience redesigning end-to-end business or operational processes in complex organizations.
• Experience working across multiple stakeholders, domains, countries or service lines.
• Experience translating strategy into operational process changes and measurable performance improvements.

Technical / Functional Skills

• End-to-end process mapping and future-state design.
• Process architecture, process governance and process performance management.
• Lean / continuous-improvement practices and practical problem-solving methods.
• KPI definition, baseline measurement and benefits tracking.
• Workshop facilitation, stakeholder alignment and implementation planning.
• Understanding of automation and AI-readiness principles from a process-design perspective.

Leadership Attributes

• Structured, pragmatic and delivery-oriented.
• Able to challenge legacy ways of working constructively.
• Strong at simplifying complexity and creating repeatable operating patterns.
• Comfortable influencing senior stakeholders and operational teams without requiring direct line authority.
• Balances standardization, control and practical adoption.