About the role

Data Scientist at Warner Music Group uncovering listener behaviors through advanced statistical methods. Building predictive models and optimizing marketing campaign impact based on data-driven insights.

Responsibilities

Build robust forecasting models across streaming, audience growth, and commercial performance. Support comprehensive forecasting efforts across deal risk, merchandise, and demand planning.
Gather and analyze audience data to establish best practices for capturing music listening preferences. Detect anomalies and surface actionable insights from large-scale streaming and engagement data.
Transform complex data into compelling stories via tailor-made presentations. Develop new hypothesis testing frameworks and decision-making tools for the real-time assessment of audience viewing patterns and music delivery streams.
Partner with local affiliates, labels, international teams, and Global Technology. Collaborate closely with merchandising teams and business stakeholders to deliver critical reporting and inference.
Interact directly with data engineering teams to identify, define, and secure the data needs required for mathematical models.

Master's degree (or foreign equivalent) in Statistics, Economics, Mathematics, or a highly related quantitative field.
2 to 6 years of professional experience in data science, analytics, or a related field (leveling will be calibrated based on tenure and technical depth).
2 to 6 years of hands-on experience working with R, Python, and SQL.
At least 2 years of experience building forecasting models across streaming, audience growth, and commercial performance, including evaluating forecast accuracy and continuously improving performance through testing, backtesting, and iteration.
At least 2 years of experience using advertising data to build marketing mix models (measuring the incremental impact of ads/campaigns using techniques like multi-linear regression and Hierarchical Bayes).
At least 2 years of experience building targeting models, including propensity scoring and look-alike modeling, to develop actionable insights into audience behavior.
At least 2 years of experience successfully deploying machine learning models into a production environment.
Proven ability to bridge the gap between technical complexity and business logic, serving as a strategic partner who empowers non-technical teams and leadership to make data-driven decisions.
At least 2 years of experience communicating complex technical concepts to non-technical stakeholders using data visualizations (e.g., matplotlib, ggplot2, or similar libraries).