Senior AI Engineer

Verizon•Alpharetta, GA

7d•$101,000 - $194,000•Hybrid

About The Position

You will join a dynamic team of propensity modelers dedicated to supporting Verizon's base management organization as it transitions to an agile pod model. As an essential member of this team, you will be assigned to a specific base management pod focused on key moments in the customer lifecycle and specific trigger events. In this role, you will be providing advanced machine learning modeling support that empowers our pods and marketing partners to unlock microsegmentation and high-level personalization, directly driving down customer churn and maximizing lifetime value.

Requirements

Bachelor's degree or four or more years of work experience.
Four or more years of relevant experience required, demonstrated through work experience and/or military experience.
Experience building, deploying, and maintaining predictive models or propensity models in a production environment.
Experience with Python, R, or similar programming languages for data science and machine learning.
Experience with SQL and database technologies for large-scale data manipulation and extraction.

Nice To Haves

Master's degree or Ph.D. in Data Science, Computer Science, Statistics, or a related quantitative field.
Knowledge of LLMs, RAG, Google ADK, and LangGraph.
Experience with Google BigQuery.
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.

Responsibilities

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