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
• 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

Strong Plus

• 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)

The Data You Will Work With

You will work with one of the most comprehensive platform analytics datasets in enterprise software - aggregated and anonymized signals spanning compute usage, storage patterns, workload composition, and cost attribution across thousands of global customers and deployments. This includes:
• Compute & credit consumption data at job and warehouse granularity
• Workload classification signals across Data Engineering, BI, Data Science, ELT, and other categories
• Account-level feature datasets with hundreds of dimensions for ML modeling
• Storage, table access, and usage tracking rollups across cloud regions and industry verticals

Why This Role Is Unique

• You will work with one of the most comprehensive platform analytics datasets in enterprise software - aggregated signals spanning petabytes of usage data across thousands of global customers
• Your advisory work will directly influence customer retention, expansion conversations, and how customers perceive the ROI of their Snowflake investment
• You will operate at the intersection of data science, economics, and cloud infrastructure - a rare combination that drives outsized impact
• This is a greenfield, high-visibility opportunity - you will define the benchmarking methodology, shape the advisory practice, and directly influence how Snowflake delivers FinOps value at scale

Location

Remote (US preferred) | Open to hybrid in San Mateo, CA or Seattle, WA

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com