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The Analytics & Insights Business Partnering Lead oversees the strategic direction of Analytics within Global Procurement, improves data-driven insights, and fosters a strong data culture to solve complex business challenges.

Lead a team delivering strategic procurement solutions for Supply Chain and Engineering, driving supplier innovation, complex cost transformations, critical negotiations, program delivery, stakeholder influence, and capability building to maximize procurement impact across Roche.

Lead AI governance for Global Procurement, defining frameworks, ethics, compliance, and value measurement. Align AI portfolio with procurement priorities, engage stakeholders, embed governance across IT, Legal, and Analytics, drive adoption and measurable ROI while overseeing performance, risk, and strategic fit of AI solutions.

Lead a global Digital & Tech procurement team to deliver strategic programs, supplier-driven innovation, cost transformations, and complex negotiations. Drive portfolio management, resource orchestration, framework design, and stakeholder influence while fostering an inclusive, high-performance culture focused on measurable procurement impact.

Lead and co-develop the Consulting practice for Diagnostics, designing and delivering large-scale organizational effectiveness and transformation initiatives, partnering with business and P&C leaders to achieve Roche 2030 ambitions and drive enterprise-wide change.

Design, prototype, and deploy production-ready AI agents and agentic workflows for procurement. Partner with procurement SMEs, IT, analytics, and vendors to move use cases from discovery to scaled rollout. Build PoCs with low-code workflow tools, ensure observability and evaluation of LLM behavior, manage delivery scope and stakeholders, and ensure enterprise readiness (security, compliance, auditability, handover). Advise on vendor/tool choices and translate technical progress into business outcomes.

The Senior Machine Learning Engineer will design, develop, and maintain AI/ML systems, optimizing algorithms and collaborating with cross-functional teams to meet diverse scientific needs.

Design, build, and operate resilient cloud and on-prem infrastructure for MLOps and HPC at global scale. Implement Infrastructure as Code, disaster recovery, autoscaling, observability, and chaos engineering. Provide technical leadership, mentor engineers, define SLAs/SLOs/SLIs, and collaborate across teams to optimize ML and HPC workloads.