Principal Scientist, Machine Learning

Universal Music•Santa Monica, CA

About The Position

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. 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.

Requirements

MS or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
8–12+ 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 (PyTorch, TensorFlow, Scikit-Learn; Transformers experience strongly preferred).
Practical experience with Generative AI and LLMs, including prompt design, evaluation, and integration into production workflows.
Experience deploying models in cloud-native environments, ideally within AWS (SageMaker, EC2, 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.

Responsibilities

Own the technical direction and execution of ML initiatives across priority business domains, including scoping, prioritization, and tradeoff decisions across competing approaches.
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.
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.
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.
Design and evaluate Generative AI use cases where unstructured data, language, or synthesis meaningfully improve outcomes.
Prototype, validate, and productionize LLM-based workflows with appropriate safeguards around accuracy, privacy, and IP protection.
Ensure that GenAI 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.
Help enforce strong data hygiene and documentation practices across the ML stack.
Define and standardize evaluation frameworks for both predictive models and GenAI 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.

Benefits

Comprehensive medical, dental, and vision coverage
100% coverage for out-patient in-network mental health services
Fertility coverage for eligible medical plan participants
Wellbeing reimbursements for fitness classes, spa treatments, meal services, travel, and so much more (up to $720/year)
Student Loan Repayment Assistance and Tuition Reimbursement
401(k) with 100% immediate vesting on the first 5% of your contributions, plus an additional UMG contribution
Flexible Paid Time Off (PTO) for exempt employees
3-weeks PTO for non-exempt employees
2-weeks paid Winter Break
10 Company Holidays (including Juneteenth and Wellbeing Day)
Summer Fridays (between Memorial Day and Labor Day)
Generous paid parental leave for every type of parent