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