- Location
- Porto, Porto
- Workplace
- Onsite
- Type
- Full-time
- Department
- Management
- Source
- RecruiterFlow
Description
On-site services such as catering, cleaning, maintenance, and facilities management.
Benefits and rewards services, including meal cards, gift cards, mobility solutions, and employee benefits.The organisation focuses on improving quality of life for employees, students, patients, and others across sectors such as corporate offices, education, healthcare, defense, and remote sites.
Purpose of the Job – State concisely the aim of the job
- Explore, build and embed practical AI and automation solutions that deliver value to Global Supply Management, including its analytics capability.
- Translate real business and operational challenges into working solutions across three practical spaces: the back-end data, user facing products, and everyday admin.
- Rapidly test and prove ideas in short cycles (weeks, not months), assess the value they create, and determine whether they should be scaled with IS&T teams, reused as lightweight solutions, or stopped.
- Act as a hands-on catalyst for responsible AI adoption, building solutions rather than simply advising, while sharing learning and raising AI capability across the global function.
- The focus is practical value: faster impact delivery, better quality, stronger adoption and clearer steer on where AI can genuinely help.
Context and main issues
▪ This is a deliberately new and exploratory role.
▪ GSM has a small but growing portfolio of AI opportunities but moving ideas into scaled IS&T led delivery too early can extend timelines, increase costs and cause opportunities to be missed.
▪ This role provides a faster route to prototype, test and prove value before involving IS&T for scaling, helping GSM identify where AI genuinely earns a place and shape the opportunity as it matures.
Organisational Context of GSM & IS&T
▪ GSM operates across global, regional and technical teams, with different processes, data maturity and local needs.
As the BI team moves to a leaner model, the challenge is to create reusable, scalable value without adding another layer of fragmented solutions.
▪ Many of the best opportunities will not arrive as clearly defined AI use cases. Stakeholders may be time-poor, sceptical or struggle to articulate where AI could help, so the role must actively uncover pain points, challenge existing ways of working and turn loosely defined needs into practical opportunities to test.
▪ Ownership of core data pipelines sits with IS&T, while GSM and BI retain responsibility for the business meaning of the data, analytics requirements, product experience, quality and adoption. The role must work effectively across that boundary to accelerate delivery without compromising ownership or standards.
▪ This role will be supported by the IS&T Global Acceleration Team (upskilling, AI community, technical support).
Example Opportunities to bring it to life
▪ Opportunity exists across the back-end data, user facing products, and everyday admin that slows teams down, including ways to make the BI team itself more efficient and effective. Existing data and models can be complex or opaque, creating further opportunities for AI to make incremental improvements in how work is simplified, automated and delivered.
▪ For instance, AI may help accelerate BI projects in areas such as planning, scoping, cleaning, data engineering, testing, documentation & user training, but it does not remove the need for sound data design or human judgement.
▪ For buyers, it may help them identify & model potential opportunities more easily. For CSR, it may help deliver complex compliance reporting faster.
Building Trust & Value
▪ AI can accelerate work, but it is not automatically the right answer. Outputs can be confidently wrong, so solutions must be grounded in trusted data, appropriate validation, clear ownership and approved security, data and AI governance.
▪ Some ideas will remain lightweight or one-off; others will justify wider industrialisation. The role holder must be able to test quickly, prove value, then make a clear call: scale, reuse, partner with IS&T / central AI teams, or stop.
Main assignments
AI use case discovery
▪ Lead rapid discovery with GSM, BI and regional teams to identify high-value opportunities where AI or automation could materially improve how work gets done.
▪ Analyse processes, pain points and recurring effort to uncover opportunities that stakeholders may not initially recognise or be able to articulate.
▪ Translate business needs into clear, testable use cases, assessing potential value, feasibility, risk and speed.
▪ Maintain a prioritised pipeline of opportunities aligned to GSM priorities and expected business outcomes.
Rapid prototyping
▪ Work in short cycles to turn prioritised use cases into working prototypes/MVPs that demonstrate the solution in practice and allow value to be tested quickly.
▪ Work hands-on across data, AI, automation and supporting technologies to build an end-to-end solution, drawing on existing tools and components where this is faster or more effective. Leverage low-code / no-code and AI-native tools where appropriate.
▪ Work within the approved technology stack and comply with applicable data-handling, security and governance requirements
▪ Select the approach and technology that best fits the problem, balancing speed, simplicity, reuse, cost and the potential to scale.
▪ Progress ideas far enough to establish whether they are technically viable, useful to users and worth further investment.
User testing, iteration and adoption
▪ Put solutions in front of real users early, gathering feedback on usefulness, accuracy, usability and trust.
▪ Improve solutions rapidly based on evidence, usage and changing business needs.
▪ Work with stakeholders to build understanding and adoption, helping users recognise where AI can enhance their work and where it cannot.
▪ Capture lessons from successful/unsuccessful tests so that learning is reused rather than repeatedly rediscovered. Prove value and determine what happens next
▪ Define simple success measures for priority use cases, such as time saved, quality, adoption, user experience or business impact, and validate outcomes through real-world use rather than technical completion alone.
▪ Test outputs and assumptions appropriately, maintaining clear human ownership of decisions and recognising where AI is not sufficiently reliable or valuable.
▪ Make clear recommendations on whether an idea should be scaled, reused as a lightweight solution, developed further, retained for a specific need or stopped.
▪ Focus further investment and formal technology involvement on opportunities with demonstrated value and a credible case to progress.
Scale responsibly and build lasting capability
▪ Prepare successful use cases for wider deployment, with clear requirements, documentation, ownership and consideration of architecture, security, data governance and ongoing support.
▪ Partner with IS&T, central AI teams and other relevant specialists to industrialise solutions where appropriate, avoiding unnecessary duplication or unsupported shadow solutions.
▪ Act as a two-way connector between GSM, regional innovation and our IT/AI networks, translating business needs into requirements that technical teams can act on and bringing relevant innovation back into GSM.
▪ Create and share reusable solutions, components, templates, patterns and lessons so successful work can be adapted across teams and regions rather than repeatedly rebuilt.
▪ Build AI capability across GSM and BI through practical demonstrations, coaching and knowledge sharing, while contributing to wider AI communities and collective learning across Sodexo.
Person Specification
Essential experience and capability
▪ A hands-on builder who is comfortable working across business, analytics and technology, and can take an idea from a loosely defined problem through to a working solution.
▪ Typically 3 to 5 years' relevant experience across analytics, data, automation, digital products or applied AI, with a strong track record of building and improving practical solutions.
▪ Strong commercial and business judgement, with the ability to understand what matters to stakeholders, recognise where value can be created, and focus technical effort on solutions that are practical, useful and worth the investment.
▪ Practical experience using Generative AI and LLM-based tools to solve real business problems, with a good understanding of their capabilities, limitations and appropriate use is a plus.
▪ Strong data and analytical foundations, including experience working with messy or complex data, data quality issues, data models and multiple information sources.
▪ Working capability in Python and SQL, or equivalent technologies, sufficient to manipulate data, automate tasks and build prototypes independently.
▪ Ability to combine AI, data, automation, APIs and low-code/no-code tools to create working solutions, selecting the simplest effective approach for the problem.
▪ Strong problem-framing and analytical skills, with the ability to uncover the real need behind an initial request, turn ambiguity into something testable, and assess value, feasibility, risk and potential to scale.
▪ Strong user focus, with the ability to test solutions in practice, respond to feedback and improve usability, accuracy and adoption.
▪ Sound judgement around testing, data handling, security, governance and responsible AI, including the confidence to challenge unreliable outputs or conclude that AI is not the right solution.
▪ Able to work with a high level of autonomy, move quickly through experimentation and adapt as both the technology and the role evolve.
▪ Strong communicator and facilitator who can explain AI in straightforward business language, engage sceptical or time-poor stakeholders, and share knowledge across business and technical teams.
▪ Fluent English.
Personal attributes
▪ Curious and pragmatic: interested in what is possible, but focused on what genuinely creates value.
▪ Comfortable with ambiguity: willing to explore problems where neither the use case nor the answer is obvious at the outset. This is a greenfield role, so the individual will be expected to bring expertise, initiative and direction as the role develops.
▪ Outcome-focused: values working solutions and measurable impact over technical complexity or AI for its own sake.
▪ Confident and constructively challenging: able to influence stakeholders, question existing approaches and push back where value is unclear.
▪ Collaborative: comfortable operating across GSM, BI, regional teams and IS&T.
▪ Continuous learner: actively keeps pace with a rapidly changing AI landscape and turns relevant developments into practical opportunities.
Helpful but not essential
▪ Tech stack: Experience with Power BI, Microsoft Fabric, Power Platform, Copilot Studio, Azure or similar enterprise data and AI platforms.
▪ Analyst: Experience with business intelligence, analytics products, self-service reporting or user-facing data solutions.
▪ Build: Experience taking prototypes through governance and into an IT-supported production environment.
▪ International: Experience working in a global or multi-country organisation.
▪ Business: Knowledge of Supply Management, procurement or another complex enterprise function.
▪ Language: Additional language capability, particularly French