AI Data Intelligence Leader — WW Channel Sales

Apples WW Channel Sales u0026 Operations organization builds AI systems that predict optimal coverage run experiments autonomously and deliver decision intelligence across all global sales an increasingly agentic world these models dont 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 CSOs 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 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. Youll 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.

Own the product vision and roadmap for CSOs decision intelligence platform spanning predictive coverage experimentation u0026 uplift anomaly detection and proactive automated insightsnDefine the ML model portfolio: predictive coverage (live and scaling globally) staffing optimization causal uplift estimation and anomaly detection from prototype through productionnDefine the framework for model and agent autonomy: which decisions they make independently which they recommend and how human feedback loops improve quality over timenDefine 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-makersnPartner 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 provenancenEstablish 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 implementnBuild the global experiments library and design workflow enabling program teams to run A/B tests measure causal uplift and make investment decisions based on evidencenEstablish measurement frameworks for model quality: accuracy calibration false positive rates and the rate at which human overrides indicate improvement opportunitiesnLead a team of data scientists building the culture and capability to ship ML- powered decision products at Apples quality barnPartner 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 pipelinesnPartner with the full-stack development team when product surfaces are needed dashboards interactive tools or user-facing features that go beyond what agents deliver autonomouslynDrive quarterly data prioritization with program owners maintaining a minimalist high-integrity data architecture that is technology-agnostic and systematically updatablenPartner with GEO teams to scale capabilities globally and with cross-functional partners (Finance Product Marketing ISu0026T AVA Platform) to integrate with Apples broader technology ecosystem

15 years in data science ML or AI product leadership with 5 years managing technical teamsnExperience owning ML model portfolios in production predictive models experimentation systems or decision intelligence products with measurable business outcomesnStrong 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 teamnStrong fluency in SQL Python and cloud data platforms (GCP/AWS)nUnderstanding of how AI agents and LLMs consume data: retrieval patterns context engineering freshness requirements and quality guarantees needed for autonomous decision makingnTrack record of treating data quality as a product feature not a cleanup tasknProven ability to lead through influence across teams you dont directly manage especially data engineering and product development teamsnProven ability to translate between technical teams and senior leadership making complex AI and data concepts concrete and decision-relevantnBS/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 forecastingnBackground in causal inference experimentation platforms or uplift modelingnExperience building or leading agentic AI systems in productionnExperience scaling ML products globally across multiple markets with varying data availabilitynUnderstanding of data privacy and governance in contexts where AI systems autonomously access and act on business datanA design-minded sensibility valuing simplicity trust and user empathy as much as model performance