Data Science Associate Manager

Job Role: Data Science Associate Manager (Customer) Team: Song AI & Data – AI & Modelling Craft, UK&I Location: London / Manchester Career Level: Associate Manager (L8) 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. With strong client relationships and deep industry expertise, we help our clients operate efficiently and sustainably through the unlimited potential of imagination, technology and intelligence.

Visit us at: https://www.accenture.com/gb-en/about/accenture-song-index The Team Within Accenture Song sits AI & Data, the practice that builds the data-led intelligence behind the customer work. Song AI & Data helps organisations unlock value from data, analytics and AI by creating more relevant, personalised and effective customer experiences. Our expertise spans customer insight, data strategy and platforms, advanced analytics, performance optimisation, and AI (including generative AI and agentic AI) transformation.

You will join the Song AI & Data UK practice, within the AI & Modelling Craft: a community of data scientists, AI engineers, modellers and solution architects focused on applying AI, machine learning and advanced analytics to solve customer and growth challenges. Our teams work across the full lifecycle, from identifying opportunities and designing solutions through to building, deploying and operating AI products that deliver measurable business value.

The Role As a Data Science Associate Manager, you will lead the technical delivery of customer-focused analytics, data science and AI solutions. This is a hands-on role where you will own the technical quality of a workstream, make key analytical and modelling decisions, and write and review code alongside the team.

Your work could include analysing customer behaviour to identify growth opportunities; developing segmentation, propensity, churn or next-best-action models; building and evaluating generative AI and retrieval-augmented generation solutions; or developing agentic workflows that improve marketing, commerce or customer service processes.

You will move between hands-on delivery and workstream leadership, guiding data scientists and AI engineers while remaining close enough to the build to be accountable for its quality. You will structure ambiguous problems, select the right analytical approach and work with product, engineering, architecture and business teams to turn ideas and insights into solutions that can be deployed, adopted and scaled.

You will also work directly with clients, translating between technical, customer and commercial perspectives. Whether explaining a churn model to a marketing team, discussing AI risks with senior stakeholders or presenting experimental results, you will help clients understand how analytics and AI can create measurable customer and business value.

What You Will Do- Design and deliver customer analytics, data science, machine learning and generative AI solutions, from problem framing and data exploration through to production and measurable client value.

• Make the technical decisions on your workstream, covering analytical approach, model and architecture selection, evaluation, guardrails, and cost and performance trade-offs.

• Apply techniques including segmentation, experimentation, propensity modelling, churn prediction, customer lifetime value and next-best-action modelling to generate insight and improve customer decisions.

• Write production-quality code and review the work of data scientists and AI engineers, including analysis, model design, code, prompts and data pipelines.

• Establish robust approaches to data preparation, experimentation, validation and performance measurement, ensuring outputs are reliable, actionable and aligned to client objectives.

• Work with engineering, architecture and product teams to take prototypes through to production, monitoring and scale using appropriate cloud and MLOps practices.

• Lead the day-to-day technical delivery of a workstream, managing quality, risks and dependencies while coaching and developing team members.

• Communicate analytical findings, technical choices, limitations and trade-offs to technical teams and senior business stakeholders, connecting the work to customer and commercial value.

• Contribute to proposals, solution approaches, estimates, reusable assets and best practices for customer-focused data and AI opportunities. What’s In It For You 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 perks such as:

• 25 days of leave to spend each year plus 3 extra volunteering days per year for charitable work of 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 Core Requirements What We Are Looking For- Hands-on data science and AI delivery in the customer domain. Experience building and delivering machine learning, generative AI or LLM-based solutions in real client or product environments, applied to customer growth, personalisation, marketing, commerce, sales or service. You can explain how a solution works, the decisions you made and the value it created.

• Strong Python capability. You can write production-quality Python and review and improve the code, analytical methods, model designs, prompts and pipelines produced by others.

• Data science and analytics fundamentals. Strong understanding of exploratory analysis, statistical methods, experimentation and machine learning, including experience developing, evaluating and deploying models. You know when traditional analytics or machine learning is more appropriate than an LLM.

• Generative AI and agentic systems. Practical understanding of generative AI architectures and techniques, including retrieval-augmented generation, LLM evaluation, prompt and context engineering, and agentic patterns. You understand their benefits, limitations and trade-offs.

• Data and production foundations. Experience preparing and integrating data for analytics and AI solutions, with practical knowledge of testing, deployment, monitoring and MLOps. You understand what it takes to operate a solution after it goes live.

• Client communication and collaboration. You can turn ambiguous business problems into focused analytical approaches and explain technical findings, choices, risks and limitations clearly to technical teams, senior client leaders and non-technical audiences.

• Responsible AI and governance. An understanding of fairness, privacy, explainability, security and human oversight, and how these considerations influence customer-facing analytics and AI solutions.

• Technical workstream leadership. Experience leading the work of data scientists, analysts or AI engineers, maintaining technical quality, coaching less experienced colleagues and taking accountability for delivery outcomes. Nice to Have- Consulting and commercial exposure. Experience contributing to proposals, estimates, solution approaches, business cases or the identification of follow-on opportunities.