Lead & Grow the Team : Manage a team of data scientists end-to-end; hiring, onboarding, personal development, performance conversations, and day-to-day support. Coach team members technically and professionally. Identify skill gaps, plan for future needs, and scale the team as the practice grows. Foster a culture of knowledge sharing, curiosity, and quality. Build, Not Just Oversee: Personally contribute to high-complexity initiatives; designing and shipping ML models, training and inference pipelines, and experimentation frameworks. Bring deep, hands-on expertise in marketing-relevant techniques: uplift modelling, propensity scoring, attribution, next best action, segmentation, causal inference, and recommender systems. Set the technical bar through code and craft, not just review. Advise Clients Strategically : Partner with account and strategy teams on scoping, proposals, and commercial conversations. you\'re a credible voice in the room when clients are deciding where to invest. Act as a trusted data advisor. Proactively identify gaps and untapped opportunities in clients\' data and marketing ecosystems. Translate complex findings into business recommendations. Anticipate business needs and challenges; build lasting client relationships as the go-to person for data science matters. Deliver End-to-End with Quality: Lead complex data science initiatives from hypothesis and experimentation through model development, validation, and activation. Collaborate closely with Data Engineers and Architects so solutions are grounded in solid infrastructure. Own quality across all team deliverables, with discipline in statistical methods, reproducibility, and insight generation. Lead the team into Agentic AI: Champion agentic AI as both a way of working and a delivery capability. Coach data scientists to adopt AI-native engineering practices (agentic coding, AI-assisted development, prompt and context engineering) and lead the design of agentic solutions for clients: from internal accelerators to production-grade agents embedded in marketing workflows. Apply CRM & Marketing Domain Expertise: Contextualize data science within clients\' CRM, campaign, and personalization strategies. Leverage hands-on experience with platforms like Adobe AEP, Salesforce Data Cloud, or Braze to extend marketing measurement and influence communications from a data perspective. Shape the Practice: Establish guidelines, ways of working, and reusable accelerators. Maintain a backlog of data science use cases. Contribute to roadmapping, maturity frameworks, and the data science product catalogue alongside the Global Data Practice Lead.