Director, AI & Machine Learning Engineering

Verizon•Basking Ridge, NJ

1d•Hybrid

About The Position

You will lead a highly sophisticated team of propensity modelers to support the base management organization as it strategically transitions to an agile pod model. Serving as the leader of the modeling chapter, you will orchestrate the deployment of data scientists across cross-functional base management pods dedicated to optimizing specific customer lifecycle stages and critical trigger events. Partner directly with marketing and business leadership to deliver advanced machine learning and modeling capabilities that unlock deep microsegmentation and hyper-personalization, directly driving down customer churn. Orchestrate all chapter operations, including strategic hiring, talent development, technical training, resource management, and process improvement. Establish, elevate, and audit modeling standards and governance across the enterprise to ensure maximum prediction accuracy and business impact. Lead cross-functional collaboration and partnership with key stakeholders across business transformation, technology, finance, and advanced analytics teams. Create and deliver compelling executive presentations that clearly translate complex AI science strategies into meaningful business narratives.

Requirements

Bachelor's degree or four or more years of work experience.
Ten or more years of relevant work experience.
Experience leading teams of data scientists or predictive modelers.
Experience with customer lifecycle marketing, base management, or churn mitigation strategies.
Experience with agile delivery frameworks or pod-based operating models.
Willingness to travel up to approximately 25% of the time.

Nice To Haves

Master's degree or PhD in data science, statistics, computer science, or a related quantitative field.
Expert-level knowledge of advanced propensity modeling, machine learning algorithms, and personalization architectures.
Experience working with large-scale cloud data platforms such as Google Cloud Platform, AWS, or Azure.
Strong proficiency in developing enterprise-level modeling standards, model governance, and process automation frameworks.
Exceptional executive communication and storytelling skills with the ability to bridge technical AI concepts and commercial business outcomes.

Responsibilities

Lead a highly sophisticated team of propensity modelers to support the base management organization as it strategically transitions to an agile pod model.
Serve as the leader of the modeling chapter, orchestrating the deployment of data scientists across cross-functional base management pods dedicated to optimizing specific customer lifecycle stages and critical trigger events.
Partner directly with marketing and business leadership to deliver advanced machine learning and modeling capabilities that unlock deep microsegmentation and hyper-personalization, directly driving down customer churn.
Orchestrate all chapter operations, including strategic hiring, talent development, technical training, resource management, and process improvement.
Establish, elevate, and audit modeling standards and governance across the enterprise to ensure maximum prediction accuracy and business impact.
Lead cross-functional collaboration and partnership with key stakeholders across business transformation, technology, finance, and advanced analytics teams.
Create and deliver compelling executive presentations that clearly translate complex AI science strategies into meaningful business narratives.