Data Decisioning Manager
Accenture UK & Ireland
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Benefits offered by this job
Job summary
Accenture Song in London is recruiting a Manager to lead NBA decisioning and marketing automation strategy for major clients. You will design and optimise decisioning architectures and coordinate delivery with offshore teams, bringing deep data science expertise and consulting capability.
You will guide data-driven outcomes, shape proposals, and mentor junior colleagues while contributing to business development and client conversations.
Qualifications
- Experience delivering NBA/NBO in commercial environments.
- Understanding of decision logic: suppression, prioritisation, arbitration.
- Strong modeling and measurement capabilities for decisioning.
Responsibilities
- Design and implement NBA/NBO decisioning frameworks and eligibility rules.
- Operationalise propensity, uplift, CLV and churn models into live decisioning.
- Define feature engineering requirements for predictive power in decisioning.
- Lead end-to-end design and delivery of decisioning solutions.
Skills
Education
Tools
Job description
London, Manchester or other UK locations
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
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 optimise 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.
- 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
- 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
- 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
- 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
- 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
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 approach is not working, you say so and bring an alternative. If the data is not fit for purpose, you define what needs to change and drive that conversation. You take full ownership — if a decisioning framework is underperforming, you diagnose it; if stakeholders are misaligned, you get them aligned. You care about whether it works, not just whether it was built.
Our Total Rewards consist of a competitive basic salary, annual performance bonus, opportunities to acquire equity and a wide range of health and wellbeing benefits. These include:
- 30 days of leave per year plus 3 extra volunteering days for charitable work of your choice
- Family-friendly and flexible work policies
- Attractive pension plan with financial wellbeing support and resources
- Private healthcare insurance plan and Mental Wellbeing support
- Employee Assistance Programme, Career Development and Counselling
- A range of generous Parental Leave offerings
SNGCR01
- Advanced Python — writing clean, production‑grade code, not just scripts (OOP, async, packaging, testing)
- Generative AI & LLM development — prompt engineering, fine‑tuning, RAG pipelines, context window management
- Agentic AI frameworks — LangChain, LangGraph, AutoGen, or similar; building multi‑step autonomous workflows
- Machine Learning — model development, training, evaluation, and deployment
- APIs & backend development — FastAPI, REST APIs, microservices architecture
- Vector databases & embeddings
- Cloud platforms — Azure GCP, or AWS; deploying and managing AI/ML workloads at scale
- Version control & MLOps — Git, CI/CD pipelines, model versioning, monitoring in production
- Data handling
- Demonstrated end‑to‑end delivery: problem → build → deployed solution → iteration
- Strong experience with Generative AI in production — not just experimentation
- A track record of building working solutions, not just prototypes or proofs of concept
- Hands‑on experience with LLM tooling (Claude, GPT‑4, Gemini, or equivalent)
- Strong analytical and problem‑solving skills — you debug fast and think in systems
- Comfortable working with messy, incomplete requirements and driving clarity through action
- English fluency and comfort working in global, cross‑functional teams
- Experience with NLP, computer vision, or multimodal AI models
- Knowledge of AI safety, guardrails, and responsible AI practices in production
- Familiarity with Snowflake, Databricks, or similar data platforms
- Prior experience acting as an AI champion or innovator within a larger organisation
- Consulting or client‑facing delivery experience
- Bachelor or Master degree in Computer Science, Data Science, Engineering, or related field
- Solution oriented mentality — sees a problem, starts building, ships, improves
- Experimental mindset — runs fast tests, learns from failure, iterates without ego
- Strong execution and delivery orientation
- Advanced AI engineering and Generative AI expertise
- Collaborative team player
- Curiosity and commitment to continuous learning
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