Structure the Machine Learning and AI area within the Data team, defining the stack, architecture, model governance, and MLOps best practices from the first model through to operating at scale
Develop and put into production predictive models using our proprietary data, focusing on content recommendation, propensity and personalization of the reader's journey (churn, reactivation, next best book, ideal format)
Bring GenAI and AI agents into daily operations and product: curation assistants, automation of internal workflows, and conversational experiences that increase engagement and productivity
Recruit, train, and develop the team of data scientists, playing a leading role in building the team that will grow with you
Work closely with Data Engineering, Analytics, Product, Growth, and Software Engineering to integrate models into products, the CRM, and business decisions, closing the full cycle from training to monitoring, retraining, and measuring impact on metrics such as D28, MAU and minutes consumed

Proven experience setting up Machine Learning or Data Science functions, from scratch or in early-stage maturity
Strong hands-on technical skills in ML, with proficiency in Python and the modern data ecosystem (e.g., Databricks, AWS, SQL)
Real ML use cases in production and at scale with measurable business impact, not just proofs of concept
Mastery of the full model lifecycle: training, deployment, performance and drift monitoring, retraining, versioning, and documentation (MLOps in practice)
Experimental mindset: A/B testing culture, causal measurement, and evidence-based decision making
Advanced or fluent English

Data Science Manager

Key skills