Principal Machine Learning Scientist

UMG Recordings; Santa Monica (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

UMG Recordings, a leader in global music, is building an Applied AI and Machine Learning function. We seek a Principal Machine Learning Scientist to lead the design and delivery of AI-enabled workflows and ML solutions that strengthen decisions across our business.

This senior IC role combines hands-on technical work with leadership across cross-functional teams. You will frame problems, select methods, and drive adoption of production-grade AI systems within UMG's diverse catalog and operations.

Qualifications

• Extensive experience developing and deploying machine learning or statistical systems, with sustained ownership of ambiguous, high-impact problems.
• Deep production experience in at least two relevant areas such as recommender systems, personalization, search and ranking, causal inference, time-series forecasting, optimization, or anomaly detection.
• Strong experience designing, evaluating, and deploying modern AI systems, including LLM-based workflows, retrieval, grounding, tool use, structured generation, safety controls, or human review.

Responsibilities

• Lead the design and delivery of AI-enabled workflows and machine learning solutions across UMG.
• Frame the right problem, choose methods, and carry solutions through to measurable adoption and business impact.
• Partner with ML engineers, data engineers, product managers, designers, domain experts, and label stakeholders to create systems people trust and use.
• Establish reusable practices for data and feature access, experimentation, model serving, monitoring, governance, lineage, and documentation.
• Mentor practitioners and raise technical standards through reusable methods, documentation, and reviews.

Skills

ML systems deployment

Recommender systems

Time-series forecasting

LLM-based workflows

Causal inference

Robust production systems

Cross-functional collaboration

Education

Advanced degree in CS/Statistics/Math/Economics/Operations Research

Job description

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:

Universal Music Group is building an Applied AI and Machine Learning function to help our teams discover and develop artists, connect with fans, make stronger commercial decisions, and protect the integrity of the music ecosystem.

We are looking for a Principal Machine Learning Scientist to turn that ambition into durable, high-impact products and decision systems. This is a senior individual-contributor role that combines hands-on technical work with technical leadership across cross-functional teams. It requires deep technical judgment, strong product sense, scientific and engineering rigor, and the ability to make progress in a complex, federated organization.

You will lead the design and delivery of AI-enabled workflows and machine learning solutions that improve decisions across UMG. Your work may span consumption and fan intelligence, catalog understanding, financial forecasting, rights, and ecosystem integrity.

You will work across business and technical teams to frame the right problem, choose the right method, and carry solutions through to measurable adoption and business impact. You will be equally comfortable developing a new model, challenging an existing approach, designing an AI evaluation framework, or helping a team turn a promising prototype into a reliable production capability.

How you'll CREATE:

• Turn ambiguous business opportunities into clearly defined machine learning, statistical, forecasting, optimization, generative AI, or non-AI problems.
• Determine whether advanced modeling adds value or whether a simpler approach is more appropriate.
• Lead initiatives from problem definition and data assessment through modeling, deployment, evaluation, monitoring, and sustained business adoption.
• Build and improve models for areas such as fan intelligence, personalization, catalog understanding, forecasting, portfolio decisions, and fraud or content integrity.
• Move generative and multimodal AI capabilities from prototypes into dependable, cost-effective workflows embedded in UMG products and operations.
• Build decision systems that identify meaningful changes, explain supporting evidence, and help stakeholders determine appropriate action.
• Define evaluation frameworks covering quality, latency, cost, robustness, privacy, provenance, copyright risk, and model safety.
• Partner with ML engineers, data engineers, product managers, designers, domain experts, and label stakeholders to create systems people trust and use.
• Establish reusable practices for data and feature access, experimentation, model serving, monitoring, governance, lineage, and documentation.
• Communicate technical tradeoffs clearly to practitioners, executives, and business leaders, adapting the level of detail to the audience.
• Influence cross-functional planning, connect capabilities across teams, and help prioritize work based on value, feasibility, risk, and learning potential.
• Mentor practitioners and raise technical standards through reusable methods, documentation, and reviews.

What success looks like in your first 12 months

• Lead one or more high-value AI or ML initiatives from ambiguous problem framing through production-or reach a well-evidenced decision not to build.
• Establish a measurable evaluation and monitoring approach that stakeholders trust.
• Improve the quality, reliability, cost efficiency, or adoption of an existing model or AI-enabled workflow.
• Help a cross-functional team make a better business decision using evidence, not only a dashboard or prototype.
• Raise the technical bar through reusable methods, documentation, reviews, and mentorship.
• Build durable partnerships across central teams, labels, and business functions.

Bring your VIBE:

• Advanced training in computer science, statistics, mathematics, economics, operations research, or another quantitative field, or equivalent applied research and industry experience.
• Extensive experience developing and deploying machine learning or statistical systems, with sustained ownership of ambiguous, high-impact problems.
• Deep production experience in at least two relevant areas, such as recommender systems, personalization, search and ranking, causal inference, time-series forecasting, optimization, or anomaly detection.
• Strong experience designing, evaluating, and deploying modern AI systems, including LLM-based workflows, retrieval, grounding, tool use, structured generation, safety controls, or human review.
• A strong record of movi