Lead, Personalization & Customer ML Strategies

On Holding AG; Zürich; Vor Ort; CHF 180.000 - 240.000; Vollzeit

Zusammenfassung

On Holding AG is seeking a Principal Data Scientist specializing in Personalization and Customer Algorithms to set the strategic direction for customer modeling across On. You will lead batch and real-time ML initiatives, training and deploying models that directly impact customers and business metrics.

You will collaborate with Product Management and Tech Partnerships to translate challenges into scalable algorithms, while advancing MLOps standards and ensuring governance across production

Qualifikationen

• 10+ years of experience building and shipping personalization engines, recommender systems, and customer-level predictive models.
• Strong ability to lead cross‑functional teams and influence senior stakeholders.
• Proven track record with rigorous experimentation and KPI impact.

Aufgaben

• Chart the vision for personalization and customer-level algorithms aligned with On’s commercial goals.
• Design, train, and deploy batch and real-time ML models in production with governance.
• Partner with Product Management and Tech Partnerships to translate business challenges into scalable algorithms.
• Drive measurable business outcomes and customer value across web, app, and messaging touchpoints.
• Evaluate and integrate new commercial technologies and partner platforms.
• Break down complex data science architectures for cross-functional alignment.
• Serve as technical authority on real-time model training, MLOps, and QA metrics.
• Mentor data science and engineering talent to raise technical standards.

Jobbeschreibung

On Holding AG is seeking a Principal Data Scientist specializing in Personalization and Customer Algorithms to set the strategic direction for customer modeling across On. You will lead batch and real-time ML initiatives, training and deploying models that directly impact customers and business metrics.

You will collaborate with Product Management and Tech Partnerships to translate challenges into scalable algorithms, while advancing MLOps standards and ensuring governance across production