# Staff Data Scientist

**Company:** [Fetch](https://hotfix.jobs/companies/fetch)
**Location:** Remote
**Role:** Data Science
**Experience:** 8+ years
**Skills:** Causal Inference, experimental design, marketing mix modeling, elasticity modeling, Python, SQL, Snowflake, dbt, Statistics, Forecasting
**Posted:** 2026-07-21

> Staff Data Scientist leading company-wide measurement, causal reasoning, and strategic modeling frameworks (MAU × ARPU, MMM, elasticity) to drive executive decisions on pricing, incentives, growth, and finance at Fetch. Owns semantic architecture, experimentation standards, and org-wide scientific leadership.

## Job Description

## What You’ll Do at Fetch
- Define and own Fetch’s company-level measurement framework anchored in MAU × ARPU.
- 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 such as pricing and incentives.
- Ensure observational and experimental results reconcile cleanly.
- Create templates and interpretation guides to standardize rigor.
- Own cross-company models that drive executive decisions, including marketing mix modeling, elasticity and incentive sensitivity, value expectation curves, strategic forecasting, and financial mechanism models supporting MAU × ARPU planning.
- 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 and thought partner for senior ICs across the org.
- Establish norms for rigorous, transparent, mechanism-driven insights.
- Apply advanced statistical and causal methods to company-level problems.
- Build scalable, production-ready analytical frameworks in partnership with engineering.
- Champion best practices in experimentation design, model validation, and reproducibility.
- Leverage modern analytics tooling such as Python, SQL, Snowflake, dbt, and experimentation platforms.

## Minimum 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.

## Preferred Qualifications
- Advanced degree in a quantitative discipline.
- Hands-on experience owning and maintaining strategic models such as marketing mix models, elasticity estimates, incentive sensitivity, or long-range forecasts used in executive planning.
- Experience in large-scale consumer products, marketplaces, ad-supported platforms, or incentive-driven systems with complex value trade-offs.
- Experience defining semantic layers, metric governance, or data contracts at scale across multiple teams or functions.
- Demonstrated track record of org-wide scientific leadership.

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