Head of Data & AI

TSC is a global, specialist advisory firm focused exclusively on sport. We operate internationally, with offices in Abu Dhabi, London, Riyadh and Singapore, combining global reach with deep local expertise. Our offer is structured across five core disciplines: Strategy, Growth and Transformation; Commercial Partnerships; Major Events and Destinations; Facilities and Venues; and TSC Legal, our in-house law firm. This integrated, end-to-end model allows us to advise across the full event lifecycle, from early strategy through to delivery and long-term impact.

Your role

As Head of Data & AI at The Sports Consultancy (TSC), you will own the foundations that make data and AI usable across the firm.

You will centralise and standardise TSC's core datasets and documents so they remain AI-ready and enable Consultants to self-serve through curated data structures and knowledge bases.

You will prototype internal accelerators and client-facing AI-enabled solutions where appropriate, while leading adoption through practical standards, training, and feedback loops that improve assets over time.

You will also own and evolve TSC's portfolio of AI and data products and act as the primary point of contact for initiatives across service lines and implementation partners.

This role combines technical execution and consulting instincts: part data engineer, part AI solutions architect, and part internal product owner working closely with leadership to deliver TSC's AI roadmap.

Your roles & responsibilities

Data Engineering & Analytics

You are the steward of TSC's data foundations. Your job is to centralise, standardise, and maintain both structured data (data warehouse) and unstructured data (knowledge bases) so Consultants can self-serve and so TSC can scale delivery with AI.

Building the data backbone

» Design and build end-to-end pipelines (ingestion * transformation * storage * serving) for priority internal datasets and recurring delivery outputs

» Establish a clear data model: definitions, schemas, keys, versioning, and lineage so data is consistent and reusable.

» Implement pragmatic quality controls (validation checks, refresh cadence, release/version workflow).

Enabling consultants to self-serve

» Create simple, reliable "front doors" to data: curated tables, standard exports, and documented datasets that consultants can use without engineering support.

» Define metadata, taxonomies, and "golden sources" so teams can find the right material fast and avoid duplicated/contradictory sources.

» Keep data and documents reliably accessible via AI (permissions-aware retrieval with traceability/citations where applicable).

Maintaining a feedback loop

» Build a repeatable "project close-out" mechanism: every engagement feeds back cleaned datasets, key assumptions, and final deliverables into the shared data foundation.

» Track reuse and quality signals; continuously refine schemas, tags, and knowledge based on real usage.

Internal & external AI product building

You own (or co-own) the technical build and prototyping loop for AI-native tools built on TSC data, both internal accelerators and client-facing prototypes.

» Prototype and ship AI-enabled workflows that reduce research, analysis, drafting, and packaging time.

» Build and maintain knowledge-base-backed systems (retrieval + structured extraction) grounded in approved sources.

» Establish practical evaluation: accuracy checks, regression testing on key workflows, and clear "human in the loop" points.

» Partner with external builders where needed, while ensuring TSC retains internal ownership of architecture decisions, data foundations, and day-2 operability.

AI-enabled advisory

You bring consulting instincts and can represent TSC as a data/AI expert in client contexts.

» Support discovery: clarify the problem, define data requirements, challenge assumptions, and map feasible solution paths.

» Translate advisory work into technical designs with explicit trade-offs (build vs buy, prototype vs scale, speed vs reliability, cost vs performance).

» Scope and support delivery of data/AI workstreams: data readiness, analytics design, AI prototype definition, and measurement.

» Help package repeatable approaches into scalable offerings without overpromising on technical capabilityAct as the bridge between TSC consulting teams and technical implementation partners.

Commercial Upsell

TSC's data and AI capability is increasingly a commercial lever, enabling the firm to extend advisory engagements into scalable technical solutions.

» Client-facing and operational capabilities to lead technical discovery and translate client needs into potential products and upsell opportunities, with a particular focus on how TSC can commercialise data

» Identify wider products or productised services for development.

» Lead rapid prototyping of data and AI solutions that demonstrate value to clients and secure buy-in for larger implementations.

» Act as the bridge between TSC's consulting practice and its technical implementation partners: owning the prototype and validation phase before handing off to partners for full-scale delivery.

» Contribute to proposals and business development where data or AI tools form part of the value proposition, helping position TSC as a firm that can deliver end to end with genuine technical expertise.

AI culture lead

You are the internal AI leader: you make adoption happen and keep quality high.

» Run enablement across service lines: playbooks, onboarding, training, office hours, and internal community.

» Define standards for prompts/workflows, documentation, evaluation, and release cycles so tools improve over time.

» Track adoption and impact (reuse rates, time saved, reduced rework, delivery consistency) and use this data to prioritise the roadmap.

Your behaviours and values

Our values underpin everything that we do. They shape our culture and help us to focus on our attitude and behaviours and inspire us to be the best version of ourselves, both individually and as a team. Success in your role also means:

» Energetic: Committed and driven

» 4-6 years in data engineering, analytics engineering, solutions architecture, or applied AI in consulting or product environments.

» Comfortable being hands-on in a lean team: you build, ship, and iterate.

» Client-facing confidence: can run technical discovery, explain trade-offs, and influence non-technical stakeholders.

» Strong SQL and data modelling & data engineering

» Proven experience turning spreadsheets into reusable datasets and/or simple data warehouse structures.

» Experience with governance basics: data quality, metadata, access control, etc.

» Working experience with LLM tooling (LLM APIs; embeddings + vector search such as pgvector/Pinecone/Weaviate; RAG/agents frameworks like LlamaIndex, LangGraph, CrewAI, etc.).

» Pragmatic understanding of LLM limitations (hallucinations, cost/latency trade-offs, permissioning, reliability).

» Experience with semantic layers / domain ontologies (turning domain concepts into reusable structures).Comfort in enterprise tool stacks (M365/SharePoint/Teams) and integrating via APIs.

» Ability to design simple, maintainable pipelines (ingest * transform * store * serve).

» Security-minded: understands the basics of permissions, role-based access, and data privacy and security.

Other skills

» Python proficiency for data manipulation and prototyping.

» Experience with lightweight product management (backlog, user testing, release cycles).

» Familiarity with modern data/AI stacks (e.g., dbt, Databricks, Azure, vector DBs).

» Sports industry data familiarity

» 25 days of annual leave plus bank holidays

» An additional day of birthday leave each year

» Monthly allowance of £40 to be used to support your health and well-being

» Travel season ticket loan of up to £2,000

» Hybrid working (typically a minimum of 3 days in the office)

» Company pension scheme

» Cycle to work scheme

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