Applied Scientist, Customer FinOps Intelligence

Snowflake•Boston, MA

2d•$134,550 - $176,597•Remote

About The Position

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. 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.

Requirements

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
Experience designing and interpreting percentile-based benchmarks and cohort analyses at scale
Strong communication and storytelling skills — able to interpret complex quantitative findings and present them clearly to both technical teams and business stakeholders
Comfort operating in ambiguous, greenfield environments where the methodology is yours to define

Nice To Haves

Prior experience at a cloud platform, SaaS analytics company, or management consulting firm working on benchmarking, telemetry analytics, or customer value modeling
Familiarity with Snowflake's platform architecture: credit model, virtual warehouses, workload types, and query execution fundamentals
Experience with Snowpark for in-platform Python ML execution
Background in FinOps, cost optimization, or cloud economics
Exposure to economic modeling or industry benchmarking methodologies
Experience presenting analytical findings to field teams or customer stakeholders (nice to have — not required)

Responsibilities

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