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Description
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.
Minimum Qualifications
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, Databricks, 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
Preferred Qualifications
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
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