Data Decisioning Manager

Data Decisioning Manager Manager (CL7) London, Manchester or other UK locations Accenture Song - Data & AI We Are Accenture Song accelerates growth and value for our clients through sustained customer relevance. Our capabilities span ideation to execution: growth, product and experience design; technology and experience platforms; creative, media and marketing strategy; and campaign, content and channel orchestration. Visit us at: https://www.accenture.com/gb-en/about/accenture-song-index The Role NBA decisioning and marketing automation are becoming critical battlegrounds for clients in telco, media, and financial services — where the volume of customer interactions is high, the cost of a wrong decision is real, and the advantage of getting it right is significant. Rules-based systems and legacy platforms are being challenged by purpose-built, AI-driven solutions. Whilst the outcome of this challenge isn't immediately clear we need someone who can navigate the complex set of options, define, design and optmise solutions for our clients. This is a strategy & delivery role. You will define strategic direction, design solutions,  and lead the optimisation of  decisioning frameworks and automation solutions for major clients. You bring deep expertise in NBA logic, data science, and decisioning architecture — and the consulting capability to turn complex challenges into working solutions. You look at a decisioning problem, ask what outcome we need, what data we have, and what model will work best design it and coordinate delivery with our  offshore and client teams. You spot issues before they become problems, raise them, and solve them. Proactively, not reactively. When something is unclear, you create clarity. When something is broken, you fix it. What You Will Do Decisioning & Data Science Design and implement NBA/NBO decisioning frameworks — eligibility rules, suppression logic, propensity score integration, offer prioritisation, and arbitration Operationalise propensity models, uplift models, CLV scores, and churn predictions into live decisioning frameworks Use value-based decisioning logic — incorporating CLV and long-term customer value into prioritisation, not just short-term conversion Measurement frameworks and optimisation Define feature engineering requirements — knowing which signals, triggers, and contextual features drive predictive power in decisioning Architect real-time decisioning solutions integrating with CRM, CDP, and data platforms Advise on technology selection — purpose-built vs platform, capex vs opex — with a forward-looking point of view Automation & Activation Design automated customer journeys across email, push, SMS, in-app, and web personalisation Design trigger-based, event-driven automation flows that respond to customer behaviour in real time Design integrations with marketing, commerce and service platforms to close the loop between model scores and actions Ensure automation is scalable, auditable, and aligned with consent and data governance requirements Delivery & Leadership Lead technical workstreams end-to-end — from design through to live deployment — owning the outcome throughout Define data input requirements for decisioning and drive data quality issues before they become delivery problems Design end to end decisioning solutions Translate model outputs and complex logic into language non-technical stakeholders can understand and trust Mentor junior team members and build decisioning capability across the practice Contribute to business development — shaping proposals and demonstrating technical credibility in client conversations What You Will Bring Required Experience in a decisioning, data science, marketing technology, or customer analytics delivery role Hands-on experience designing and delivering NBA/NBO solutions in a commercial environment Deep understanding of decision logic: suppression, fatigue management, prioritisation, and arbitration Experience with using one or more platforms in a decisioning solution — Pega CDH, Salesforce Marketing Cloud, Adobe Target/AEM/Campaign, Braze, Iterable, or purpose-built solutions Strong working knowledge of predictive modelling for decisioning: propensity, churn, CLV, uplift, and incrementality Experience designing statistically valid champion/challenger and multivariant tests and holdout methodologies Ability to critically evaluate model performance in a business context Experience designing measurement frameworks that prove genuine incremental value SQL proficiency; Python or R for data exploration, model validation, and decisioning diagnostics Hands-on automation experience — trigger-based journeys, event-driven architecture, consent management Solution-oriented and proactive — you define the path forward, raise issues early, and drive progress without being pushed Consulting or client-facing delivery experience is a strong advantage Exposure to GenAI applications in decisioning — personalised content generation, AI-driven offer selection Preferred Experience evaluating purpose-built decisioning solutions and contributing to capex/opex business cases Familiarity with real-time streaming technologies in a decisioning context Awareness of data clean rooms and privacy-preserving analytics for audience targeting Bachelor's or Master's degree in a quantitative, technology, or business-related field Who You Are You are a technical specialist who solves problems and delivers outcomes. You have spent enough time in decisioning systems — the data pipelines, the model outputs, the business logic, the edge cases — to know what makes them work in practice, not just in theory. When a client brings you a challenge, you do not wait for someone else to frame the solution. You get into the data, form a hypothesis, and start moving. You are solution-oriented in the most practical sense: focused on the outcome, not the process. If the approac