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.

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.

Benefits

Snowflake is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Flexible work arrangements.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLPythonpandaspolarsscikit-learnstatsmodelsunsupervised MLclusteringPCAanomaly detection

Soft Skills

communicationstorytellinganalytical interpretationcollaborationadaptability

Certifications

MS in StatisticsPhD in StatisticsMS in Applied MathematicsPhD in Applied MathematicsMS in EconometricsPhD in EconometricsMS in Computer SciencePhD in Computer Science