Remote Staff Data Scientist: Causal Analytics & Measurement

Fetch; United States; On-site; USD 190,000 - 240,000; Full time

Job summary

Fetch is seeking a Staff Data Scientist to lead company-wide measurement, causal inference, and strategic modeling. You will define canonical metrics, own MAU × ARPU frameworks, and shape executive reporting with scalable analytics.

In this role you’ll collaborate with product, growth, marketing, and finance to build production‑ready frameworks, validate high‑risk tests, and raise the scientific maturity of the data science organization across the company.

Qualifications

• 8+ years of experience in data science, economics, statistics, or applied research, including experience operating at Staff or Principal scope on company and/or org-level problems.
• Deep expertise in causal inference, experimental design, and observational analysis, with demonstrated ownership of high-stakes business decisions informed by causal evidence.
• Experience defining and owning company-level measurement frameworks, canonical metrics, or strategic models used by senior leadership.
• Proven ability to influence and support executive decision-making, including presenting trade-offs, uncertainty, and recommendations that directly impact strategy.
• Exceptional written and verbal communication skills, with the ability to explain complex causal and modeling concepts to non-technical senior audiences.
• Bachelor’s degree in a quantitative field.

Responsibilities

• Define and own Fetch’s company-level measurement framework anchored in MAU × ARPU.
• Company Measurement and Causal Strategy.
• Establish decision frameworks for pricing, incentives, and value trade‑offs.
• Set standards for evidence quality, uncertainty, and confidence in decision‑making.
• Define the causal reasoning model used across product, growth, marketing, and finance.
• Own the scientific capability roadmap including elasticity, value curves, MMM, and forecasting.
• Architect the semantic mart and metric logic powering FetchGPT and scalable insights.
• Define canonical metric definitions and unify logic across experimentation platforms, dashboards, and diagnostics.
• Partner with Analytics Engineering and Data Platform to build foundational data assets.
• Establish BI standards and eliminate redundant or conflicting dashboards.
• Serve as the quality bar for high‑impact analytics and diagnostics.
• Review strategic analyses to ensure correct interpretation and mechanism alignment.
• Set scientific rules for experimentation and validate high‑risk tests.
• Ensure observational and experimental results reconcile cleanly.
• Create templates and interpretation guides to standardize rigor.
• Own cross‑company models driving executive decisions, including marketing mix, elasticity and incentive sensitivity, value curves, forecasting, and financial mechanism models.
• Raise the scientific maturity of the data science and analytics organization.
• Design upskilling programs in statistics, causality, modeling, and storytelling.
• Author best‑practice modeling libraries and documentation.
• Serve as a technical anchor for senior ICs across the org.
• Establish norms for rigorous, transparent, mechanism‑driven insights.

Skills

Causal inference

Experimental design

Observational analysis

Executive communication

Data science

Education

Bachelor’s degree in a quantitative field

Advanced degree in a quantitative discipline

Tools

Python

SQL

Snowflake

dbt

Experimentation platforms

Job description

Fetch is seeking a Staff Data Scientist to lead company-wide measurement, causal inference, and strategic modeling. You will define canonical metrics, own MAU × ARPU frameworks, and shape executive reporting with scalable analytics.

In this role you’ll collaborate with product, growth, marketing, and finance to build production‑ready frameworks, validate high‑risk tests, and raise the scientific maturity of the data science organization across the company.