We are:

Accenture Song accelerates growth and value for our clients through sustained customer relevance. Our capabilities span from 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 at the speed of life through the unlimited potential of imagination, technology and intelligence. Visit us at: www.accenture.com/song

The role:

As a Data Orchestration Manager within the Data & AI practice at Accenture Song, you will lead teams delivering decision science, experimentation, and AI-driven decisioning that improves HCP and patient experiences and drives measurable commercial impact. You will partner with strategy, creative, product, engineering, and Life Sciences stakeholders to frame business problems into decision frameworks and operationalize analytics and AI solutions across omnichannel engagement, field, digital, and journey touchpoints, in a privacy and compliance-aware environment.

Core skills:

• Decision framing and hypothesis-driven problem solving
• Experimentation and causal measurement (A/B testing, holdouts, incrementality)
• Applied machine learning (propensity, uplift, segmentation, recommendations/next-best-action)
• Strong statistical rigor and model validation
• Executive-ready storytelling and stakeholder influence
• Team leadership, delivery management, and cross-functional collaboration
• Python and SQL; analytics-to-production mindset (pipelines, monitoring, governance)

Key responsibilities:

• Lead client-facing delivery (scope, plan, quality, risks, and team leadership).
• Translate business objectives into decision frameworks, success metrics, and analytics roadmaps.
• Design and guide solutions such as next-best-action, personalization/recommendations, segmentation, propensity/uplift models, churn/retention, and journey optimization.
• Own measurement and experimentation strategy to quantify impact (test design, incrementality, causal methods).
• Partner with data and ML engineering to productionize solutions (data requirements, pipelines, model monitoring, retraining triggers, governance).
• Communicate insights and recommendations to senior stakeholders with clear implications and actions.
• Contribute to proposals and practice-building (playbooks, accelerators, reusable assets).