ML Engineer / Data Scientist, Applied AI

Machine Learning Engineer – AI Transformation

Reporting to the VP Data Solutions & Innovation within the Business Intelligence organization.

This role is the technical engine of our AI transformation. You will be responsible for bringing our most impactful AI models out of the lab and scaling them into reliable, high-performance production systems.

Hybrid (#LI-Hybrid).

Key Responsibilities

• Rapid Modeling & Experimentation: Design, develop, and benchmark state-of-the-art machine learning models (forecasting, segmentation, recommendation, NLP, etc.) with a strong emphasis on quick iteration and scientific validation of new concepts.
• Generative AI & Exploration: Lead hands‑on technical exploration into advanced techniques, including LLMs, RAG architectures, and Generative AI applications to create new forms of automated analysis and augmented intelligence products.
• Production Engineering & MLOps: Translate validated prototypes into robust, production‑ready specifications, and lead the implementation of MLOps best practices (CI/CD, monitoring, serving) required for the reliable deployment of models.
• Complex Data & Feature Engineering: Deeply explore complex, multi‑modal data (e.g., high‑dimensional data, text, time series) defining the necessary features and data pipelines to support highly accurate experimental models for strategic analysis.
• Cross‑Functional Collaboration: Work closely with the Product Manager, Data Scientists, and business stakeholders to ensure technical solutions maximize tangible business impact and adhere to ethical AI standards.
• Technology Scouting: Drive innovation through hands‑on exploration of new AI technologies, including LLMs, GenAI, and vector databases, and evaluate their practical application to our music and operational data.
• Knowledge Transfer: Contribute to AI adoption and technical literacy across the company through clear documentation, workshops, and knowledge sharing with both technical and non‑technical teams.

Skills & Experience

• Education: Bachelor’s degree required in Applied Mathematics, Computer Science, Software Engineering, or a highly technical quantitative discipline. A Master’s degree (MS) or higher is strongly preferred.
• Experience: 2+ years of professional experience as a Machine Learning Engineer, Applied ML Scientist, or similar role, with a clear focus on productionizing models and advanced AI techniques.
• Technical Depth: Strong expertise in Python development and established skills in deploying and managing the full lifecycle of complex ML/DL models. Experience with advanced analysis of unstructured or multi‑modal data (e.g., high‑dimensional feature vectors, dense embeddings) is highly valued.
• MLOps Mindset: Proven track record of transforming R&D proofs‑of‑concept into robust, scalable, and monitored production‑grade ML solutions.
• Engineering Rigor: A background in software engineering best practices (clean code, testing, Git) is essential.
• Communication: Exceptional ability to communicate complex concepts and model limitations clearly and effectively to product and non‑technical stakeholders.
• Domain Affinity: High curiosity and enthusiasm for music, entertainment, or culture is a strong plus.

WMG is committed to inclusion and diversity in all aspects of our business. We are proud to be an equal opportunity workplace and will evaluate qualified applicants without regard to race, religion or belief, age, sex, sexual orientation, gender, gender identity or gender reassignment, marital or civil partnership status, disability, pregnancy, childbirth or any other characteristic protected by law.

#J-18808-Ljbffr…