Audible maintains a team of roughly 75 data scientists and machine learning engineers worldwide who use AI to automate decisions across pricing, content investment, listener segmentation, and product experiments. This team's work shapes what content gets acquired, how products are priced, and which listeners see which recommendations.

The Global Insights & Data Science team uses machine learning and deep learning techniques across many areas of the business, including recommendation systems, pricing optimization, A/B experimentation, marketing ROI measurement, and content investment decisions. The economics sub-team applies causal inference methods — including techniques known as synthetic control, Synthetic Difference-in-Differences (SDID), and Bayesian approaches — to measure the impact of business decisions and set pricing strategy. Machine learning pipelines run on Amazon Web Services (AWS) infrastructure, including services called SageMaker, Batch, Lambda, and Step Functions. Job postings describe the team as having "an integral role in the design and integration of models to automate decision making throughout the business in every country."