Senior AI Engineer

Verizon Verizon · Telecom · Basking Ridge, NJ +2

Senior AI Engineer role focused on developing, training, and deploying advanced propensity models to predict customer behaviors, churn risk, and lifecycle triggers for Verizon's base management organization. The role involves collaborating with pods to unlock microsegmentation and personalization strategies, monitoring model performance, and partnering with data engineering teams to scale ML workflows. Requires experience in building and deploying predictive models in production, Python/R, and SQL.

What you'd actually do

  1. Developing, training, and deploying advanced propensity models to predict customer behaviors, churn risk, and lifecycle triggers.
  2. Collaborating cross-functionally with assigned base management pods to translate complex business problems into actionable data science solutions.
  3. Unlocking microsegmentation and personalization strategies by translating model outputs into tailored customer journeys for marketing execution.
  4. Monitoring, evaluating, and refining model performance over time to ensure high accuracy, relevancy, and business impact.
  5. Partnering with technology and data engineering teams to scale machine learning workflows and production systems.

Skills

Required

  • Python
  • R
  • SQL
  • building, deploying, and maintaining predictive models or propensity models in a production environment

Nice to have

  • Master's degree or Ph.D. in Data Science, Computer Science, Statistics, or a related quantitative field
  • Knowledge of base management structures, customer lifecycle stages, or churn prevention strategies
  • Experience working in an Agile environment or within cross-functional, pod-based operating models
  • Experience with cloud platforms such as GCP, AWS, or Azure, and cloud-native machine learning tools (e.g., Vertex AI, SageMaker)
  • Experience with machine learning framework ecosystems (e.g., TensorFlow, PyTorch, Scikit-Learn)
  • Strong communication skills with the ability to explain complex machine learning models and metrics to non-technical stakeholders

What the JD emphasized

  • production environment
  • advanced machine learning modeling support
  • advanced propensity models

Other signals

  • propensity models
  • customer churn
  • personalization
  • production environment