Principal Economist, GTM Science

THE ROLE

Everpure is seeking a Principal Economist to drive go-to-market (GTM) efficiency by leading our causal measurement science vision. You will lead the development of advanced statistical methodologies to measure the incrementality of sales headcount, incentive investments, and customer-facing activities (e.g. workshops, proofs of concept, enablement collateral, events). In this high-impact role, you will pioneer advanced statistical frameworks that separate true sales-driven lift from market noise, directly shaping global headcount planning and incentive designs.

Collaborating closely with executive GTM Finance, Data Science, and Sales leadership, you will transform massive datasets into production-grade science deliverables that maximize global revenue and optimize ROI.

• 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.

• 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 causal Tree, EconML, or stats models) 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.
• Location:
  
  We are primarily an in-office environment and therefore, you will be expected to work from the Santa Clara, CA office in compliance with Everpure’s policies, unless you are on PTO, work travel, or other approved leave.

The Annual Base Salary Range Is $211,000—$338,000 USD

• Innovation:
  We celebrate those who think critically, like a challenge, and aspire to be trailblazers.
• Growth:
  We give you the space and support to grow along with us and to contribute to something meaningful.
• Team:
  We build each other up and set aside ego for the greater good.

And because we understand the value of bringing your full and best self to work, we offer a variety of perks to manage a healthy balance, including flexible time off, wellness resources, and company-sponsored team events.

Candidates with disabilities may request accommodations for all aspects of our hiring process.

Everpure is proud to be an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or any other characteristic legally protected by the laws of the jurisdiction in which you are being considered for hire.