Marketing Data Scientist – Data & AI Enablement

🕒 May 13

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Vonage

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2001

📡 Telecommunications

☁️ SaaS

🤖 Artificial Intelligence

💰 $225M Post-IPO Debt on 2014-08

Telecommunications • SaaS • Artificial Intelligence

Vonage is a global leader in communications services, offering a robust suite of tools including Communications APIs, Unified Communications, and Contact Centers solutions. The company empowers businesses to reach and engage customers across various channels such as messaging, voice, and video, facilitating superior customer experiences. Vonage integrates advanced technologies like AI and automation to enhance operational efficiency and customer satisfaction, supporting industries ranging from healthcare to retail with tailored communications solutions. As a testament to its capabilities, Vonage is consistently recognized as a leader in Communications Platform as a Service (CPaaS).

📋 Description

• Collaborate closely with the Snowflake engineering team to define, spec, and validate data requirements for models and AI applications • Serve as the translation layer between data engineering and AI/ML teams, ensuring data is structured, clean, and optimized for downstream use • Enable and support agentic AI workflows by designing and maintaining the data layers that power agent memory, context retrieval, and tool-use • Build and maintain data models and transformation logic on top of Snowflake to support predictive and generative AI use cases • Identify and resolve data quality issues in collaboration with the engineering team • Partner with marketing science and analytics teams to ensure data assets are accessible and model-ready • Automate and streamline data validation processes to improve reliability of AI-facing data pipelines

🎯 Requirements

• 8 years of experience in Data Science or a data engineering-adjacent role • Advanced proficiency in Python and SQL • Deep expertise in Snowflake - including data modeling, Snowpark, and advanced querying • Approximately 2 years of hands-on experience powering agentic AI workflows with data (e.g., RAG pipelines, structured data for LLM tool-calling, agent memory and context layers) • Familiarity with vector databases or embedding-based retrieval systems (e.g., Pinecone, Weaviate, pgvector) • Experience collaborating with or working closely alongside data engineering teams • Familiarity with cloud data platforms (AWS, GCP, or Azure)

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off