Company Overview
Snowflake powers the AI Data Cloud—a global network that enables thousands of organizations to mobilize their data with unmatched scale, concurrency, and performance. Within this ecosystem, businesses can unify siloed data, discover and securely share governed data, and run diverse analytical workloads seamlessly.
No matter where data or users reside, Snowflake delivers a consistent and unified experience across multiple public clouds. Its powerful platform serves as the backbone of the AI Data Cloud, providing a comprehensive solution for data warehousing, data lakes, data engineering, data science, application development, and data sharing.
Join the growing community of Snowflake customers, partners, and data providers who are transforming their businesses with the power of the AI Data Cloud.
As a Principal Full Stack Data Scientist at Snowflake, you will:
- Collaborate closely with Product Management and Engineering to shape feature roadmaps, define key metrics, and develop analytical workflows for measuring success.
- Design efficient data models and build high-quality production pipelines, working alongside Engineering to ensure proper telemetry.
- Develop scalable analytics, machine learning, and causal inference frameworks to analyze feature usage, identify performance improvements, and enhance customer experience.
- Serve as an early adopter and expert user of Snowflake features, providing valuable feedback on design and functionality.
- Determine the best way to present insights—whether through Snowflake Notebook, Worksheets, Snowsight Dashboards, or Streamlit Apps—for maximum impact.
- Influence critical product and engineering decisions by leveraging data-driven insights to improve customer outcomes.
- Provide key reports and insights to the executive team for board presentations and industry publications.
- Take a creative and resourceful approach to solving complex, often unstructured, problems.
Ideal Candidate Qualifications
The perfect fit for this role will have:
- A Master’s or Ph.D. in a quantitative field such as Mathematics, Statistics, Operations Research, Economics, Engineering, or Computer Science.
- 12+ years of hands-on experience in data science or a closely related field.
- Expert proficiency in SQL and Python, with hands-on experience using scikit-learn, NumPy, and pandas.
- Extensive experience working with large-scale machine-generated data, including logs, application telemetry, and customer usage data.
- Deep expertise in MPP databases such as Snowflake, Redshift, BigQuery, and Vertica.
- Strong data storytelling abilities to effectively communicate insights to business leaders and technical teams.
- The ability to thrive in a fast-paced, dynamic environment, demonstrating adaptability and a willingness to tackle any challenge necessary for success
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