Jobs at Metyis

Lead complex programmes and portfolios involving multidisciplinary teams, technology, data, digital commerce, and business transformation. Responsibilities include defining governance and delivery strategies, managing budgets, risks, dependencies, resources, vendors, and benefits, and reporting to executive stakeholders. The role leads project managers, facilitates steering committees, supports client growth and business development, and drives delivery excellence, organizational transformation, and measurable business outcomes. International travel is required as needed.

Configure and optimize the o9 platform for client business needs across demand planning, supply planning, inventory optimization, and financial planning. Develop IBPL configurations and dashboards, collaborate with cross-functional teams, troubleshoot issues, and ensure performance and scalability. The role requires strong knowledge of o9 modules, dimensions, measures, procedures, rules, and supply chain planning processes.

Provides proactive executive assistance to a Senior Partner, Executive Board Member, and three Partners. Responsibilities include complex calendar, travel, meeting, and priority coordination; supporting business continuity and CEO coverage; managing stakeholders and projects; and coaching junior team members through feedback, evaluations, and development planning.

Work with clients and internal teams to elicit, analyze, document, and manage business and technical requirements. Facilitate workshops, model processes, create BRDs, user stories, use cases, and functional specifications, and bridge business stakeholders with delivery teams. Support Agile delivery, testing, UAT, solution validation, risk management, requirements traceability, change adoption, and continuous improvement across digital transformation and technology initiatives.

Plan, execute, monitor, and optimize multi-channel CRM campaigns. Manage AEM-related business requirements, prioritize technical work, troubleshoot operational issues, and collaborate with technical teams. Provide guidance, training, and mentorship while analyzing campaign performance and delivering actionable insights. The role requires stakeholder coordination, platform expertise, strong organization, and basic web technology knowledge.

Lead the implementation of Data and AI Governance for clients, translating ambiguous governance challenges into practical operating models. Define ownership and accountability, facilitate stakeholder workshops, embed data quality, metadata, lineage, privacy, security, and responsible AI practices, and support governance forums. Create reusable templates and playbooks while collaborating with Business, Data, AI, Architecture, Privacy, and Security teams across complex enterprise environments.

Lead client implementations of Data and AI Governance, establishing ownership, stewardship, quality, metadata, lineage, privacy, security, and responsible AI practices. Facilitate stakeholder workshops, translate ambiguous governance challenges into practical roadmaps, embed governance into daily operations, support AI governance decisions, and create reusable templates and playbooks. Collaborate with Business, Data, AI, IT, Architecture, Privacy, and Security teams across complex enterprise environments.

Lead data science consulting projects, translate business problems into analytics solutions, manage client relationships, oversee project delivery, identify growth opportunities, and guide junior team members. The role involves working across the data lifecycle, applying statistical and machine learning expertise, presenting insights to business stakeholders, and coordinating multidisciplinary teams.

Lead data science consulting engagements, manage client relationships, translate business problems into analytics solutions, oversee project delivery, and guide analytical teams. Advise senior business leaders, communicate complex concepts clearly, identify growth opportunities, and mentor junior team members. The role requires strong statistical, programming, database, visualization, and machine learning expertise, along with substantial consulting or industry experience and project leadership.

Leads data science projects for client organizations, translating business problems into impactful analytics solutions. Manages client relationships, provides project oversight, identifies new opportunities, presents analytical findings, and guides junior team members. The role involves the full data lifecycle, including acquisition, cleaning, integration, analysis, interpretation, visualization, and machine learning, while coordinating multidisciplinary teams and communicating complex concepts to business stakeholders.

Own the end-to-end delivery of AI products, translating business needs into technical specifications, prioritizing use cases, and coordinating agile implementations. Manage acceptance testing, quality assurance, data governance, production integration, stakeholder alignment, and project governance. Monitor deployed AI performance, identify improvements, support adoption through documentation and communication, and ensure AI solutions deliver measurable client and business value.

Gather and document business, functional, and non-functional requirements through workshops, interviews, and process analysis. Translate stakeholder needs into solution requirements, user stories, process models, and project documentation. Collaborate with product, project, architecture, engineering, design, and QA teams throughout delivery. Support backlog refinement, testing, UAT, change impact analysis, requirements traceability, risk management, and solution adoption while contributing to business analysis standards and continuous improvement.

Supports client engagements and manages workstreams or smaller projects across digital transformation, technology, data and AI, commerce, and business transformation initiatives. Maintains project plans, schedules, RAID logs, status reports, governance documentation, and delivery dashboards. Coordinates multidisciplinary teams, facilitates meetings and Agile ceremonies, monitors risks and dependencies, supports stakeholder communication and change management, and contributes to process improvement, proposals, and project planning.

Lead client data governance implementation by identifying stakeholders and governance gaps, defining ownership and decision forums, facilitating business-technical workshops, and embedding governance into daily operations. The role translates ambiguous challenges into actionable approaches, develops reusable templates and playbooks, and collaborates with data, analytics, architecture, privacy, and security specialists. It requires stakeholder engagement, data governance or related experience, data platform understanding, and strong communication skills.

Lead client data governance implementation by identifying governance gaps, defining ownership roles, facilitating stakeholder workshops, translating ambiguous challenges into actionable approaches, and embedding governance into daily operations. Develop reusable templates, playbooks, and implementation accelerators while collaborating with data, analytics, architecture, privacy, security, and business consulting specialists.

Designs and governs scalable AI architecture across cloud, data, and application environments. Responsibilities include defining architectural standards, advising on AI solution design, overseeing integrations and deployments, monitoring infrastructure performance and costs, coordinating technical stakeholders, supporting specifications and acceptance testing, and mitigating technical risks. The role requires hands-on experience with cloud platforms, AI/ML systems, DevOps, data architecture, containers, CI/CD, infrastructure as code, and monitoring.

Lead the architecture, integration, scalability, reliability, and governance of enterprise AI systems. Establish technical standards, advise on AI solution design, oversee specifications and testing, manage cloud infrastructure performance and costs, coordinate cross-functional teams, and mitigate technical risks. The role requires strong experience in DevOps, cloud architecture, AI tooling, data platforms, infrastructure as code, CI/CD, containers, and major cloud providers.

Support the development, testing, deployment, and improvement of AI solutions, including LLM applications, RAG pipelines, prompts, AI agents, and orchestration workflows. Prepare data and knowledge foundations, research emerging models and frameworks, translate business needs into AI use cases, monitor solution performance, and document methodologies. Collaborate with senior technical professionals, data teams, and business stakeholders in an international environment.

Design and support scalable AI infrastructure, applications, data pipelines, and cloud-based solutions. Collaborate with technical and business stakeholders, contribute to architecture and documentation, support deployments and testing, monitor performance and costs, troubleshoot issues, and help manage technical risks. The role requires cloud, DevOps, data engineering, infrastructure-as-code, containerization, CI/CD, monitoring, and AI/ML platform experience.

Supports the development, testing, deployment, and monitoring of AI solutions. Responsibilities include preparing data and knowledge foundations, building RAG pipelines, testing prompts and context strategies, researching AI models and tools, contributing to AI agents and orchestration workflows, translating business needs into use cases, and documenting solutions. The role collaborates with data, AI, technical, and business teams while continuously improving AI application performance and reliability.