Job Template (UK)

Why This Role Exists

Multiverse is evolving from a services-led organisation into an AI-first, platform-led workforce transformation company. Our 200+ coaches are the mechanism through which learning translates into measurable business outcomes. To scale to $1bn+ in bookings, the system that deploys those coaches against variable demand must be as disciplined as the coaching itself. Today, workforce management, capacity planning, and allocations operate through manual processes, fragmented data, and institutional knowledge. The function works, but it does not scale. This role exists to build the supply engine that fuels delivery. Not delivery itself.

Role Overview

You will translate demand forecasts into coach deployment, build and operate the matching engine that pairs coaches with learners, and lead the analytical intelligence layer across the full operations machine. You are not building a reporting team. You are building an operational engine: automated and rule-based for routine allocations, with a designed escalation path for complex matches where delivery leadership makes the final call. The analytics function you lead will serve as the shared data backbone across coach platform, process automation, support, fulfilment, and the delivery organisation.

Primary Responsibilities

• Build and Run the Allocations Engine (~35%): Define, build, and operate the system that translates demand into coach deployment. Own time-to-fill, allocation process cost, and bench and contractor flex decisions that keep the supply model solvent. Design the matching engine: routine, low-risk allocations run automatically; delivery owns the final decision where coach capability, learner context, and programme nuance require human judgment. Iterate the engine as the delivery model evolves toward Operating Model 2.0, including non-standard capacity modelling for new programmes, customisations, and DPT scaling.
• Lead Ops Analytics as the Central Intelligence Layer (~35%): Coordinate dedicated analytical resources (one data Senior Manager, three analysts) as the shared data backbone across the full ops machine. Standing outputs: utilisation, bench duration, forward demand coverage, forecast accuracy, operating model performance. Surface the right data to the right consumers in the right cadence.
• Own the Cross-Functional Interface (~15%): Ingest demand plans from finance and RevOps, translate into supply plans, produce resource-required projections that feed hiring and budget decisions. Serve as the named counterpart to delivery leadership on match quality trade-offs; own the stakeholder feedback loop on allocation quality. Build the tools and RACIs to decentralise budget ownership across individual VPs and Senior Directors.
• Scenario Planning & Strategic Projects (~10%): Ad hoc modelling: operating model transition scenarios, restructure modelling, cost-to-serve deep dives, DPT operationalisation business cases.
• Protect the Build (~5%): Steer the systems and tooling investment that moves this function from manual to automated.