Apple's WW Channel Sales & Operations organization builds AI systems that predict optimal coverage, run experiments autonomously, and deliver decision intelligence across all global sales programs. In an increasingly agentic world, these models don't just inform human decisions - they power autonomous agents that act on them at global scale. This role owns the product vision and ML strategy for the decision intelligence platform that enables both people and agents to make better decisions, faster.
You will own CSO's decision intelligence platform end-to-end: defining what it should do, building the ML models that power it, and scaling it globally. This spans predictive coverage models, an experimentation and uplift engine, a unified data management system across all sales programs, and the real-time visibility layer that surfaces automated insights to program leaders.\n\nThe primary consumers of your models and intelligence layer are AI agents that make autonomous decisions. This changes what data quality means, what latency is acceptable, and how systems need to be designed. You'll lead a team of data scientists while partnering with a separate data engineering team for pipeline and infrastructure work, and a separate full-stack development team for product surfaces - though increasingly, agents will handle much of the integration and delivery work themselves.
15+ years in data science, ML, or AI product leadership, with 5+ years managing technical teams\nExperience owning ML model portfolios in production - predictive models, experimentation systems, or decision intelligence products with measurable business outcomes\nStrong understanding of production data systems (Spark, Databricks, Kafka, Airflow, Snowflake, or equivalent) - sufficient to define requirements, set quality contracts, and partner effectively with a data engineering team\nStrong fluency in SQL, Python, and cloud data platforms (Google Cloud Platform/AWS)\nUnderstanding of how AI agents and LLMs consume data: retrieval patterns, context engineering, freshness requirements, and quality guarantees needed for autonomous decision making\nTrack record of treating data quality as a product feature, not a cleanup task\nProven ability to lead through influence across teams you don't directly manage - especially data engineering and product development teams\nProven ability to translate between technical teams and senior leadership - making complex AI and data concepts concrete and decision-relevant\nBS/MS in Computer Science, Data Engineering, or related discipline
Experience with predictive analytics in retail, channel, or field operations - coverage models, staffing optimization, or demand forecasting\nBackground in causal inference, experimentation platforms, or uplift modeling\nExperience building or leading agentic AI systems in production\nExperience scaling ML products globally across multiple markets with varying data availability\nUnderstanding of data privacy and governance in contexts where AI systems autonomously access and act on business data\nA design-minded sensibility - valuing simplicity, trust, and user empathy as much as model performance
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 90733111
- Position Id: d8c69681c7a6b272822d27f2993464ce
- Posted 8 hours ago
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