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Leads resourcing governance insights for Product Development Data Sciences by consolidating workforce, financial, sourcing, portfolio, and operational data. Develops executive analyses, presentations, scenarios, recommendations, and competitive intelligence to support resource decisions. Monitors trends, risks, anomalies, and capacity needs while partnering with operations and analytics infrastructure teams to improve data quality, dashboards, automation, and advanced analytics.

Build robust, scalable scientific software, applications, agents, data pipelines, and data ecosystems supporting research biology, drug discovery, translational medicine, and AI/ML. The role involves full-stack development, cloud-native architectures, APIs, microservices, event-driven systems, data integration, search, visualization, and collaboration with scientists, data engineers, product leaders, and ML operations teams.

Provide legal counsel to Roche’s UK and Ireland businesses, advising on commercial pharmaceutical law, data protection, compliance, clinical trials, contracts, marketing, competition, anti-bribery, and disputes. Manage data subject requests and privacy incidents, support R&D and clinical trial teams, negotiate agreements, monitor legislative changes, mitigate legal risk, and maintain legal templates and tools. The role requires an England and Wales-qualified lawyer with 2–6 years’ post-qualification experience and life sciences expertise.

Leads cross-functional programs from planning through execution, governance, adoption, and value realization. Manages roadmaps, milestones, risks, documentation, metrics, and executive reporting while coordinating technical, operational, and business stakeholders. Designs change-management and adoption strategies, conducts impact assessments, addresses resistance, and develops communication plans to embed solutions into business operations. Experience in data science, advanced analytics, or AI/ML initiatives is valued.

Design, prototype, evaluate, and deploy AI agents and agentic workflows that improve global procurement processes. Partner with procurement, engineering, IT, analytics, governance, and external vendors to translate business needs into reliable enterprise solutions. Own solution delivery from discovery through rollout, including workflow design, integrations, observability, value tracking, risk management, and stakeholder communication. Build proofs of concept with low-code tools and guide scalable production implementations while ensuring privacy, compliance, auditability, and operational readiness.

Serves as the primary procurement partner for Roche’s Product Development organization. Builds customer relationships, gathers business insights, promotes procurement solutions, improves end-to-end procurement experiences, develops operational strategies, drives value, supports compliance and adoption, and leads projects or workstreams across a global, matrixed environment.

Leads strategic procurement programs across commercial and corporate services, including category transformations, cost optimization, major negotiations, supplier innovation, and complex cross-functional initiatives. Develops data-driven frameworks, influences senior stakeholders, and delivers market-leading procurement solutions. The role also builds enterprise networks, coaches teams, and promotes collaboration, continuous improvement, and psychological safety across Global Procurement.

Design and develop scalable full-stack software, data ecosystems, interactive drug discovery tools, and AI-enabled platforms for scientific research. Lead cloud-native architecture using microservices and event-driven patterns, integrate scientific data and computational workflows, establish engineering excellence across the SDLC, collaborate with research scientists, and mentor developers. The role requires deep backend, frontend, data engineering, cloud, and AI-assisted development expertise.

Build and maintain infrastructure, data systems, and tools for evaluating and deploying AI/ML models supporting scientific drug discovery. Partner with scientists, product managers, ML experts, and DevOps engineers to define requirements, implement algorithms, optimize GPU performance, develop cloud-native systems, and own features through production. Contribute to architecture, testing, code reviews, documentation, and engineering practices while mentoring and collaborating across teams.

Build and maintain scalable AI/ML scientific applications, backend systems, data infrastructure, and model evaluation tools for drug discovery. Partner with scientists and product leaders to define requirements, implement algorithms, optimize performance, and shape architecture and engineering practices. The role requires expertise in machine learning, statistics, GPUs, Kubernetes, databases, data lakes, and cloud-native systems, with preferred experience in biological or imaging data, research environments, workflow automation, GenAI, or agents.