AI Data Intelligence Leader — WW Channel Sales

Apple•Austin, TX

About The Position

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. The 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.

Requirements

15+ years in data science, ML, or AI product leadership, with 5+ years managing technical teams
Experience owning ML model portfolios in production — predictive models, experimentation systems, or decision intelligence products with measurable business outcomes
Strong understanding of production data systems (Spark, Databrates, Kafka, Airflow, Snowflake, or equivalent) — sufficient to define requirements, set quality contracts, and partner effectively with a data engineering team
Strong fluency in SQL, Python, and cloud data platforms (GCP/AWS)
Understanding of how AI agents and LLMs consume data: retrieval patterns, context engineering, freshness requirements, and quality guarantees needed for autonomous decision making
Track record of treating data quality as a product feature, not a cleanup task
Proven ability to lead through influence across teams you don't directly manage — especially data engineering and product development teams
Proven ability to translate between technical teams and senior leadership — making complex AI and data concepts concrete and decision-relevant
BS/MS in Computer Science, Data Engineering, or related discipline

Nice To Haves

Experience with predictive analytics in retail, channel, or field operations — coverage models, staffing optimization, or demand forecasting
Background in causal inference, experimentation platforms, or uplift modeling
Experience building or leading agentic AI systems in production
Experience scaling ML products globally across multiple markets with varying data availability
Understanding of data privacy and governance in contexts where AI systems autonomously access and act on business data
A design-minded sensibility — valuing simplicity, trust, and user empathy as much as model performance

Responsibilities

Own the product vision and roadmap for CSO's decision intelligence platform — spanning predictive coverage, experimentation & uplift, anomaly detection, and proactive automated insights
Define the ML model portfolio: predictive coverage (live and scaling globally), staffing optimization, causal uplift estimation, and anomaly detection — from prototype through production
Define the framework for model and agent autonomy: which decisions they make independently, which they recommend, and how human feedback loops improve quality over time
Define the requirements, contracts, and quality standards for the unified data management system — a structured data foundation spanning all global sales programs that serves both ML models and human decision-makers
Partner with the dedicated data engineering team to design and prioritize data pipelines that serve real-time decision loops and batch analytical workloads, with clear contracts for freshness, completeness, and provenance
Establish data quality as a safety concern — when models and agents act autonomously on data, bad data produces wrong decisions at scale. Define observability, lineage, and automated drift detection requirements for the data engineering team to implement
Build the global experiments library and design workflow — enabling program teams to run A/B tests, measure causal uplift, and make investment decisions based on evidence
Establish measurement frameworks for model quality: accuracy, calibration, false positive rates, and the rate at which human overrides indicate improvement opportunities
Lead a team of data scientists — building the culture and capability to ship ML-powered decision products at Apple's quality bar
Partner with the data engineering team to translate model requirements into pipeline priorities, data contracts, and quality SLAs — you define what data is needed and to what standard; they build and operate the pipelines
Partner with the full-stack development team when product surfaces are needed — dashboards, interactive tools, or user-facing features that go beyond what agents deliver autonomously
Drive quarterly data prioritization with program owners — maintaining a minimalist, high-integrity data architecture that is technology-agnostic and systematically updatable
Partner with GEO teams to scale capabilities globally, and with cross-functional partners (Finance, Product Marketing, IS&T, AVA Platform) to integrate with Apple's broader technology ecosystem