Analyze large-scale consumption, workload, and performance datasets to uncover insights, trends, and optimization opportunities.
Build unit economic models such as cost per query, cost per TB scanned, cost per user, efficiency benchmarks across workloads.
Explore and interpret internal metadata pipelines created by Product/Data Science teams to understand compute, storage, and pipeline behaviors.
Translate quantitative findings into clear, actionable insights that help customers reduce waste and improve ROI.
Develop reusable analytical frameworks for consumption modeling and workload optimization.
Partner with account teams, Sales Engineers, and Value Engineers to support customer conversations with data-backed recommendations.
Build customer-facing deliverables using Jupyter notebooks, dashboards, and executive-ready PowerPoint narratives.
Continuously refine analytical approaches to improve accuracy, benchmarking quality, and scale of FinOps engagements.

Requirements

3–5+ years of experience in data science, quantitative analysis, analytics engineering, or applied statistics roles.
Strong SQL skills — ability to query, aggregate, model, and interpret large datasets.
Strong Python skills (pandas, numpy, data modeling, exploratory analysis).
Solid understanding of statistics, experimentation, time series, optimization techniques, or benchmarking.
Experience working with large-scale datasets from data warehouses, cloud environments, or analytics platforms.

Benefits

Applicant Tracking System Keywords

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

SQLPythonpandasnumpydata modelingexploratory analysisstatisticsexperimentationtime seriesoptimization techniques

analytical thinkingcommunicationcollaborationproblem-solvingcustomer engagement