R&D Transformation Lead

This role owns the two biggest levers for how the R&D organisation evolves: who it works with externally and how it builds capability internally — including the adoption of AI-driven ways of working. The Vendor, Org & AI Transformation Specialist ensures subcontractor quality does not become a delivery liability, that the organisation is structured and skilled for what is ahead, and that AI tools are embedded into engineering workflows in a disciplined, measurable way.

Qualifications

Must-Have:

• Engineering or Masters in engineering with 10–14 years in R&D, engineering program management, or technical operations
• Direct experience in managing hardware/embedded subcontractor engagements DAC, BOM/AVL, DER, ECR/ECO in a hardware-software context
• Demonstrated experience driving AI or Gen AI tool adoption in an engineering organization — structured rollout with measurable outcomes, not just awareness.
• Comfortable working across engineering, HR, IT, and procurement stakeholders
• Proficient in org and people processes — headcount planning, competency frameworks, onboarding design

Nice-To-Have:

• Familiarity with Nokia or comparable OEM engineering processes
• Experience with Microsoft Copilot, GitHub Copilot, or equivalent GenAI tools in an engineering context
• Background in organizational design or HR business partnering. Exposure to Nokia BBD or Fixed Networks program structures

Responsibilities

• Manage weekly and monthly R&D operating cadence, including program reviews, SOW standards, Deliverable Acceptance Checklists (DAC — SW and HW categories), and Payment Gate Schedules.
• Conduct technical audits and deliverable reviews for subcontractor engagements across SW, HW, and systems categories. Track vendor performance KPIs; manage underperformance with structured Corrective Action Plans (CAPs)
• Oversee the R&D governance calendar, ensuring timely and data-driven discussions occur.
• Identify, pilot, and scale AI tools (Copilot, generative AI assistants, automated test generation, AI-assisted code review) within R&D teams
• Triage escalations by identifying blockers early and coordinating resolution efforts with engineering teams.
• Facilitate day-to-day Agile execution, including Sprint Planning, SCRUM, and retrospectives. Define and monitor delivery KPIs, identifying bottlenecks and implementing process improvements.
• Drive quality metrics monitoring and ensure adherence to engineering quality gates throughout the product lifecycle.
• Conduct root cause analysis on quality issues and foster communities of practice around engineering excellence.