Marketing Data Scientist, Data & AI Enablement
Join Vonage and help us innovate cloud communications for businesses worldwide!
Why this role matters:
We are looking for a Data Scientist who speaks the language of both data and AI - and knows how to make them work together. You will serve as the critical bridge between our Snowflake data platform and the AI-powered systems that drive our marketing and go-to-market motions. Your work will ensure that our data is shaped, structured, and ready to power the next generation of agentic AI applications, enabling autonomous, intelligent systems that act on behalf of our teams and customers.
Your key responsibilities:
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
What you'll bring:
Deep fluency in Snowflake - comfortable living in it daily as a power user, and confident enough to hold technical conversations with Snowflake engineers
A strong understanding of how data must be structured and governed to reliably power AI and machine learning systems
The ability to translate business and modeling needs into precise, actionable data requirements for engineering teams
A collaborative mindset with the ability to operate effectively across data engineering, AI, and marketing science functions
A proactive, self-starter approach capable of navigating ambiguous data environments and proposing pragmatic solutions
Required:
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)
Tools & Technologies:
Snowflake - Snowpark, dynamic tables, advanced SQL querying, data modeling
Python - pandas, NumPy, SQLAlchemy, Jupyter
LLM & Agentic AI Frameworks - LangChain, LlamaIndex, AutoGen, or similar
Vector Databases - Pinecone, Weaviate, pgvector, or Chroma
Cloud Platforms - AWS (S3, Lambda, SageMaker), GCP (BigQuery, Vertex AI), or Azure equivalents
Orchestration & Pipeline Tools - dbt, Apache Airflow, or Prefect (as a collaborator/power user)
Version Control - Git/GitHub or GitLab
Data Quality & Observability - Monte Carlo, Great Expectations, or similar
API & Integration Tools - REST APIs, Webhooks, JSON/YAML fluency for connecting data to AI systems
What we consider a plus:
Experience with orchestration tools such as dbt, Airflow, or similar
Exposure to marketing data ecosystems (CDP platforms, ad platform data structures)
Experience in a B2B SaaS environment
Advanced degree (Master's or PhD) in a quantitative field such as Computer Science, Statistics, or Data Engineering
#LI-JS3
Disclaimer: The posted range represents the good faith salary for this role at the time of posting. Final compensation is determined by factors including (but not limited to) geographic location, relevant experience, specific skill sets, and internal equity.
There’s no perfect candidate. You don't need all the preferred qualifications to make a valuable impact on our team. Our employees and customers come from diverse backgrounds, so if you're passionate about what you could achieve at Vonage, we'd love to hear from you.
To learn how we process your personal data during the recruitment process, please refer to our Privacy Notice.
Who we are:
Vonage is a global cloud communications leader. And your talent will further help brands - such as Airbnb, Viber, WhatsApp, and Snapchat - accelerate their digital transformation through our fully programmable-based unified communications, contact center solutions, and communications APIs. Ready to innovate? Then join us today.
Note: The purpose of this profile is to provide a general summary of essential responsibilities for the position and is not meant as an exhaustive list. Assignments may differ for individuals within the same role based on business conditions, departmental need or geographic location.
Apply To This Job
Why this role matters:
We are looking for a Data Scientist who speaks the language of both data and AI - and knows how to make them work together. You will serve as the critical bridge between our Snowflake data platform and the AI-powered systems that drive our marketing and go-to-market motions. Your work will ensure that our data is shaped, structured, and ready to power the next generation of agentic AI applications, enabling autonomous, intelligent systems that act on behalf of our teams and customers.
Your key responsibilities:
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
What you'll bring:
Deep fluency in Snowflake - comfortable living in it daily as a power user, and confident enough to hold technical conversations with Snowflake engineers
A strong understanding of how data must be structured and governed to reliably power AI and machine learning systems
The ability to translate business and modeling needs into precise, actionable data requirements for engineering teams
A collaborative mindset with the ability to operate effectively across data engineering, AI, and marketing science functions
A proactive, self-starter approach capable of navigating ambiguous data environments and proposing pragmatic solutions
Required:
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)
Tools & Technologies:
Snowflake - Snowpark, dynamic tables, advanced SQL querying, data modeling
Python - pandas, NumPy, SQLAlchemy, Jupyter
LLM & Agentic AI Frameworks - LangChain, LlamaIndex, AutoGen, or similar
Vector Databases - Pinecone, Weaviate, pgvector, or Chroma
Cloud Platforms - AWS (S3, Lambda, SageMaker), GCP (BigQuery, Vertex AI), or Azure equivalents
Orchestration & Pipeline Tools - dbt, Apache Airflow, or Prefect (as a collaborator/power user)
Version Control - Git/GitHub or GitLab
Data Quality & Observability - Monte Carlo, Great Expectations, or similar
API & Integration Tools - REST APIs, Webhooks, JSON/YAML fluency for connecting data to AI systems
What we consider a plus:
Experience with orchestration tools such as dbt, Airflow, or similar
Exposure to marketing data ecosystems (CDP platforms, ad platform data structures)
Experience in a B2B SaaS environment
Advanced degree (Master's or PhD) in a quantitative field such as Computer Science, Statistics, or Data Engineering
#LI-JS3
Disclaimer: The posted range represents the good faith salary for this role at the time of posting. Final compensation is determined by factors including (but not limited to) geographic location, relevant experience, specific skill sets, and internal equity.
There’s no perfect candidate. You don't need all the preferred qualifications to make a valuable impact on our team. Our employees and customers come from diverse backgrounds, so if you're passionate about what you could achieve at Vonage, we'd love to hear from you.
To learn how we process your personal data during the recruitment process, please refer to our Privacy Notice.
Who we are:
Vonage is a global cloud communications leader. And your talent will further help brands - such as Airbnb, Viber, WhatsApp, and Snapchat - accelerate their digital transformation through our fully programmable-based unified communications, contact center solutions, and communications APIs. Ready to innovate? Then join us today.
Note: The purpose of this profile is to provide a general summary of essential responsibilities for the position and is not meant as an exhaustive list. Assignments may differ for individuals within the same role based on business conditions, departmental need or geographic location.
Apply To This Job