Data Science & Machine Learning - Platform Operational Excellence - Stage M - W - N H/F - 75

Believe is one of the world's leading companies in the digital music market.
Believe's mission is to support local artists and labels in the digital ecosystem by providing them with solutions at every stage of their career and development.

With over 2,037 employees across more than 50 countries, our teams support artists with expertise, respect, fairness, and transparency. To sustain our strong growth across all continents, we are constantly looking for new Believers.

Join us so that together, we can make a strong and positive impact on the music industry !

Believe is listed on compartment A of the regulated market of Euronext Paris (Ticker : BLV, ISIN : FR0014003FE9).
Ready to #setthetone with Believe?Operational Excellence (OpEx) in cloud operations focuses on optimizing processes and systems to improve productivity, performance, and efficiency. Our Platform team adopts a product-oriented approach, delivering self-service solutions to meet the diverse needs of our internal engineering teams. By continuously assessing and refining our practices, we drive consistent improvement and automation across the organization.

Attached to the Head of Operational Excellence, you will join our Platform team as a Data Science & Machine Learning Intern. In this role, you will contribute to various data-driven projects aimed at enhancing our platform operations. Your responsibilities will include classifying incidents and services, develop a comprehensive service catalog, and create data quality indicators. You will explore and apply advanced data mining and AI/ML techniques (such as clustering, NLP, and anomaly detection) to support continuous improvement and automation within the platform.

Activities :
- Collaborate with cross-functional teams (platform engineers, SREs, product owners, etc.) to understand operational data and classify incidents and services, improving our incident management and service discovery processes.
- Build and refine a software service catalog (about 200 services) by analyzing existing data, standardizing entries, and applying machine learning where appropriate to ensure accurate and up-to-date documentation of our services.
- Develop and implement data quality indicators and dashboards to monitor and report on the accuracy, completeness, and consistency of our platform data.
- Experiment with data mining and machine learning techniques (e.g., clustering, natural language processing, anomaly detection) to extract insights from operational data and optimize platform workflows.
- Document your methodology and share findings with the team to drive data-informed decision-making and continuous improvement initiatives.