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Senior Strategist
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About R/GA
R/GA is an independent creative innovation company built for the intelligence age. We harness the power of design and technology to create more valuable experiences for people and brands. From architecting adaptive brand experiences with AI to optimising complex systems for real-world impact, we help organisations anticipate change and shape what comes next. Our teams combine craft, curiosity and technology to deliver work that drives both business and human impact.
About the Role
This is a strategic, hands-on role at the intersection of product and experience strategy, audience research, measurement, evaluation, and Answer Engine Optimisation (AEO) - spanning both traditional digital products (websites, apps) and generative, AI-driven experiences. You will be responsible for shaping how users experience the brand, anchoring those experiences in deep audience insights, and setting the strategy that makes digital and adaptive AI systems measurable, testable, and continuously improvable.
Beyond just evaluating performance, you will actively help ideate solutions and shape product strategy, ensuring that as brands communicate across channels and intelligent interfaces, their outputs are resonant, accurate, and rigorously optimised. The remit involves translating audience research, brand intent, and experience strategy into actionable product features, evaluation frameworks, and measurement systems that prove (and improve) real-world performance.
You'll help guide projects from audience discovery and solution ideation through to deployment and evaluation, bridging the gap between human-centric design, technical rigour, and strategic accountability. We are seeking a practitioner who values the craft of rigorous testing across both established digital products and emerging generative experiences.
This role defines the next generation of strategy and analytics. We are transitioning from retrospective reporting towards real-time, evaluation-driven orchestration. You will architect the brand's measurement and evaluation layer across websites, apps, and an increasingly agentic landscape - building the testing infrastructure and AEO discipline that ensures brand experiences are visible to answer engines, verifiably accurate, and continuously validated at scale.
On any given day, you might:
- Contribute to Product & Experience Strategy: Translate audience research and user insights into actionable product strategies, actively helping teams ideate new features, user journeys, and AI-driven solutions across channels.
- Synthesise Audience Research: Leverage market research and user insights to build audience personas, uncover user intent, and ensure product ideation is grounded in real human needs.
- Run AEO Audits: Conduct Answer Engine Optimisation (AEO) audits to identify where brand content is missed, misread, or misrepresented by AI systems and answer engines.
- Set Evaluation Strategy: Define the frameworks and rubrics used to score AI outputs for accuracy, brand alignment, and intent-fulfilment.
- Scope Testing: Set the strategy and criteria for synthetic test scenarios (1,000+ simulated paths), then interpret the results to surface resilience issues, edge cases, and failure modes.
- Set Data Strategy: Define the semantic data models and JSON-LD schema approach that make brand content and user intent machine-readable and measurable by AI/answer engines.
- Interpret Economics: Report on operational metrics (latency, cost, accuracy, hallucination rate), interpreting trends against evaluation benchmarks to guide decisions.
- Interpret Performance: Build evaluation dashboards and scorecards, turning results into clear findings and recommendations for teams and leadership.
- Validate Behaviour: Review system prompts and intervention protocols against evaluation results to confirm brand strategy is being encoded correctly.
- Narrate Strategy: Translate technical performance data and audience insights into high-level strategic narratives and reporting for executive stakeholders.
- Collaborate Broadly: Partner with data engineering, creative, and technology teams to ideate solutions, define what "good" looks like, and report on when it's being achieved.
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You'd be the right fit if you:
- Prioritise Audience Needs & Intent: Have experience grounding digital products and experiences—from websites and apps to generative, AI-driven interfaces—in deep audience research and user intent.
- Are Evaluation-Minded: You're fascinated by how AI systems succeed and fail, and see rigorous testing as a core discipline, not an afterthought.
- Think in AEO: Understand how content and brand signals are discovered, parsed, and surfaced by AI and answer engines—and how to measure that visibility.
- Ideate & Solve Systemically: Bring a structured, hypothesis-driven approach to problem-solving; you actively help ideate strategic solutions rather than just reporting on isolated metrics.
- Translate Strategy into Metrics: Can turn abstract brand strategy and audience insights into traceable, measurable evaluation criteria.
- Thrive in Agile: Are comfortable working in a fast-moving, iterative testing and product-development environment.
- Value Rigour: Take pride in continuous improvement, statistical soundness, and the fine details of a measurement framework.
- Guard the Brand: Understand the tension between dynamic personalisation and brand consistency, and know how to test for and catch AI 'hallucinations' before they reach users.
You bring:
- Experience: 4–6 years in Product/Experience Strategy, Evaluation, Measurement/Analytics, AI Operations, or Marketing Sciences with a strong measurement focus, spanning both traditional digital products and generative AI experiences.
- Experience Strategy POV: Background in product strategy, CX/UX strategy, or customer journey design, with a track record of using audience research to ideate solutions and bringing that lens to how AI systems are evaluated and measured.
- Audience & Market Research Literacy: Familiarity with qualitative and quantitative market research platforms and methods, and how they're used to build personas, uncover intent, and inform product ideation.
- Channel & Comms Strategy: Experience using audience and channel research to help teams determine which channels, platforms, and AI interfaces are the right fit for reaching a given audience.
- Technical Foundation: Proficiency in SQL and Python for data manipulation, statistical analysis, and building evaluation pipelines.
- Evaluation Tooling: Working familiarity with AI evaluation and observability tools such as LangSmith, Arize, or equivalent, and the ability to interpret their outputs.
- Agentic Systems: Familiarity with agentic workflows and orchestration frameworks (e.g. LangChain, LlamaIndex, or similar), including how to evaluate and test multi-step, autonomous AI behaviour.
- AEO Literacy: Practical understanding of how answer engines and LLM platforms (ChatGPT, Claude, Gemini, Perplexity) surface, cite, and rank content—and how to audit and improve that visibility.
- Data Comfort: Comfortable working with data in various forms (structured, unstructured, qualitative, quantitative, including JSON / JSON-LD) and translating it into visualisations that make findings engaging and accessible to non-technical audiences.
- Testing Frameworks: Experience designing test suites, golden datasets, rubrics, or scoring frameworks for evaluating AI outputs at scale.
- Visual Mapping: Working knowledge of workflow and diagramming tools (Figma, Lucidchart, Miro) for documenting evaluation logic, user journeys, and decision trees.
- Technical Intuition: A strong understanding of how digital systems work (and fail), and how to design measurements that catch failure early.
- Cross-Functional Track Record: Demonstrated experience proactively communicating research findings and technical evaluation results to technical, creative, and business audiences alike to spark ideation.
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Bonus:
- Prompt Engineering: Familiarity with advanced prompting techniques (Chain-of-Thought, Few-Shot) and how they affect evaluation outcomes.
- Structured Data: Experience building or working with databases, vector stores, or knowledge graphs used in retrieval and citation by AI systems.
- Statistical Methods: Background in experiment design, A/B testing, or statistical significance testing applied to AI system performance.
- Economic Awareness: Understanding of AI operational costs and performance trade-offs (latency vs. accuracy vs. cost).
- AEO/SEO Crossover: Experience with traditional SEO or content strategy, adapted to how answer engines discover and cite content.
This role is based in London and requires in-office collaboration three days per week: Wednesday, Thursday, and one additional day of your choice. Candidates must be located in the London area or willing to relocate before their start date.
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