Principal Economist, GTM Science

Pure Storage•Chicago, IL

2d•Onsite

About The Position

This role is for a Principal Economist to drive go-to-market (GTM) efficiency by leading the causal measurement science vision. The individual will lead the development of advanced statistical methodologies to measure the incrementality of sales headcount, incentive investments, and customer-facing activities. This high-impact role will pioneer advanced statistical frameworks to separate true sales-driven lift from market noise, directly shaping global headcount planning and incentive designs. The role involves collaborating closely with executive GTM Finance, Data Science, and Sales leadership to transform massive datasets into production-grade science deliverables that maximize global revenue and optimize ROI.

Requirements

PhD in Economics, Econometrics, or an equivalent quantitative discipline, such as Applied Economics or Quantitative Marketing, preferred.
Advanced Econometric & Causal Inference Expertise: Deep grounding in causal inference methodologies, including causal forests, treatment-effect heterogeneity, synthetic control, and difference-in-differences analyses.
Statistical Computing Fluency: Advanced proficiency in R, Python, or comparable statistical computing environments (such as causalTree, EconML, or statsmodels) to write clean, repeatable, and production-ready code.
Data Engineering & Production Deployment Experience: Demonstrated capability to architect, deploy, and scale production-grade causal analyses utilizing messy, complex business datasets (such as CRM, financial systems, or telemetry data) in partnership with data engineering teams.
Executive-Level Communication & Stakeholder Influence: Exceptional capacity to translate complex econometric and statistical results into clear narratives and strategic recommendations for non-technical corporate leaders.

Responsibilities

Own the Go-To-Market Causal Measurement Strategy: Architect and execute the long-term roadmap for measuring sales incrementality, partnering with senior Sales, Finance, and Operations executives to deliver highly accurate, actionable insights that guide global investment decisions.
Design and Deploy Advanced Causal Models: Build, validate, and scale state-of-the-art observational models (such as causal forests, panel methods, and uplift models) in R or Python to isolate true headcount-driven lift and incentive plan performance from external market demand and noise.
Drive Investment ROI and Strategic Allocation: Quantify the incremental impact of sales headcount, incentive modifications, and customer-facing programs (e.g., proofs of concept, workshops) for executive leaders (CFO, CRO, CMO) to directly influence headcount planning, NRR growth, and resource allocation.
Operationalize Production-Grade Data Pipelines: Partner with Data Engineering, Analytics, and GTM Operations to establish robust, stable, and maintainable pipelines and data infrastructure, translating complex econometrics into automated dashboards and reusable frameworks for continuous business planning.