Applied Scientist, Customer FinOps Intelligence

About the Role

Snowflake sits at the center of the world's data — powering thousands of organizations across every industry. This role exists to prove and communicate the business value Snowflake delivers to its customers — through rigorous analysis of platform telemetry, not anecdote or assumption.

As an Applied Scientist on the Customer FinOps Intelligence team, you will mine aggregated, anonymized platform usage signals to answer three foundational questions: How are customers using Snowflake? How efficiently are they using it? And where are they leaving value on the table? Your analysis will surface opportunities for smarter feature adoption, more efficient workload design, and stronger unit economics — creating momentum for customers to get more from their Snowflake investment while strengthening Snowflake's retention and expansion story.

You will build the analytical models, benchmarking frameworks, and peer comparison methodologies that translate raw platform signals into compelling, data-driven insights — collaborating closely with field teams to ensure findings are communicated with clarity and acted upon at scale.

What You Will Do

• Develop and maintain peer benchmarking models using platform usage signals to produce unit economic metrics: Credits per 1,000 jobs, Credits per TB scanned, Workload mix (% spend on Data Engineering, BI, Data Science, ELT, etc.), Cost efficiency percentiles (p25 / p50 / p75 / p90) by industry and customer segment
• Construct peer groups using unsupervised ML techniques (clustering, dimensionality reduction) on account-level feature vectors — combining industry vertical, usage fingerprint, and size normalization into meaningful comparable cohorts
• Engineer a benchmarking feature store from large-scale platform usage datasets using Snowpark and dbt, covering compute, storage, and workload dimensions at account and industry level
• Apply statistical rigor to handle skewed distributions, outlier accounts, and temporal variation in usage patterns across a highly diverse customer base
• Package benchmarking outputs into repeatable advisory assets — cost optimization playbooks, benchmarking dashboards, and narrative summaries — that can be consumed by field teams and scaled across the customer base
• Partner with Account Executives, Solution Engineers, and Customer Success Managers to embed FinOps benchmarking into the customer lifecycle — translating analytical outputs into field-ready narratives and customer conversations
• Collaborate cross-functionally with Product, FinOps, and Sales Strategy to ensure advisory insights feed back into product priorities and field positioning

What We Are Looking For

Must Have

• MS or PhD in Statistics, Applied Mathematics, Econometrics, Computer Science, or a quantitative field
• 5+ years of hands-on experience in applied data science, quantitative research, or value engineering — ideally at a cloud platform, enterprise SaaS, or management consulting firm
• Expert-level SQL — comfortable with complex multi-join queries across billions of rows of operational metadata
• Strong proficiency in Python (pandas/polars, scikit-learn, statsmodels) for statistical modeling and ML
• Deep experience with unsupervised ML: clustering (k-means, DBSCAN, hierarchical), PCA/UMAP, anomaly detection