We are UMG, the Universal Music Group. We are the world’s leading music company. In everything we do, we are committed to artistry, innovation and entrepreneurship. We own and operate a broad array of businesses engaged in recorded music, music publishing, merchandising, and audiovisual content in more than 60 countries. We identify and develop recording artists and songwriters, and we produce, distribute and promote the most critically acclaimed and commercially successful music to delight and entertain fans around the world. How we LEAD:To design, lead, and scale high-impact machine learning systems that directly support UMG’s forecasting, automation, and strategic decision-making capabilities. You will translate ambiguous business problems into well-defined modeling strategies, set technical direction across multiple ML initiatives, and act as a senior technical authority within UMG’s applied ML organization. You are both a deep individual contributor and a force multiplier for the broader team, operating as a key partner to the SVP of Machine Learning. How you’ll CREATE:Applied ML Leadership:Own the technical direction and execution of ML initiatives across priority business domains, including scoping, prioritization, and tradeoff decisions across competing approaches. You will evaluate problem structure and data characteristics to determine when traditional ML, Generative AI, or deterministic approaches are most appropriate, in alignment with UMG’s “right tool for the job” framework. Design, build, and productionize machine learning models across the full lifecycle—from feature engineering and training to deployment, monitoring, and retraining. You will proactively identify model degradation, concept drift, and regime changes driven by market or business shifts. Serve as the technical lead across complex, multi-workstream modeling efforts, setting standards, reviewing architecture decisions, and ensuring consistency across teams and use cases. Mentor and elevate senior and junior scientists alike, shaping modeling standards, review practices, and technical rigor across the organization. Partner closely with Finance, Data, Product, and Engineering teams to integrate ML outputs into driver-based financial models, dashboards, and decision-support tools. You will help stakeholders understand why models move, not just that they move. Act as a senior advisor to business and technical leadership, helping shape how ML is applied to UMG’s highest-priority problems. Generative AI & Advanced Modeling:Design and evaluate Generative AI use cases where unstructured data, language, or synthesis meaningfully improve outcomes. You will prototype, validate, and productionize LLM-based workflows with appropriate safeguards around accuracy, privacy, and IP protection. Ensure that Gen. AI systems are anchored in reliable data and complemented by deterministic or predictive models where appropriate, minimizing reliance on autonomous deployments that cannot be readily validated or explained. Partner with Legal, Privacy, and Data teams to ensure all models meet UMG’s standards for ethical use, explainability, and data lineage. You will help enforce strong data hygiene and documentation practices across the ML stack. Define and standardize evaluation frameworks for both predictive models and Gen. AI systems, ensuring consistent measurement of performance, risk, and business impact. Contribute to defining success metrics for ML initiatives and support ROI measurement through accuracy gains, operational efficiency, revenue lift, or risk reduction. Bring your VIBE:MS or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field .–12 years of experience building and deploying machine learning systems in production, including ownership of complex, high-impact initiatives. Strong proficiency in Python and modern ML libraries (Py. Torch, Tensor. Flow, Scikit-Learn; Transformers experience strongly preferred). Practical experience with Generative AI and LL - Ms, including prompt design, evaluation, and integration into production workflows. Experience deploying models in cloud-native environments, ideally within AWS (Sage. Maker, EC 2, Glue). Demonstrated ability to operate with high autonomy in ambiguous environments and influence senior stakeholders across technical and business domains. Ability to clearly explain modeling tradeoffs and results to technical and non-technical partners.