Meet Fetch AI & Data
AI & Data at Fetch sit at the center of how we understand our business, make decisions, and build intelligent products. The organization operates as an integrated AI & data ecosystem, spanning multiple disciplines, including data engineering, analytics engineering, machine learning, experimentation, and data platforms, all working together to turn data into durable business and customer impact.
Teams operate in complex problem spaces where requirements evolve, tradeoffs are constant, and the right answer is rarely obvious. Success depends on strong technical judgment, comfort with ambiguity, and the ability to gather context and make informed decisions while balancing quality, performance, scalability, and responsible use.
Practitioners across this org contribute hands-on to production systems, analytical foundations, and intelligent features. You will collaborate closely with product, platform, and engineering partners, help shape standards and best practices, and ensure our AI and data capabilities scale reliably as Fetch grows.
About the Role:
You'll help Fetch run on rigorous performance standards and scalable self-service analytics. You'll help deliver our core company KPI framework, including its underlying model, while leading reusable, company-wide analytical frameworks and automation.
The work centers on trusted metrics: standardizing how impact is measured and connecting product and business drivers to outcomes like MAU, retention, ARPU, revenue, and margin. You'll validate forecasts and communicate sensitivity and uncertainty, so evidence turns into planning and operating decisions.
You’ll own defined KPIs, modeling, or measurement workstreams through delivery and adoption, make actionable recommendations, and translate company goals into workstream targets. You’ll work with cross-functional partners to build reproducible diagnostics and analytical tools, and document and share reusable methods with the team.
What You'll Do at Fetch:
- Independently own a company wide KPI, modeling, or measurement workstream through delivery and adoption
- Contribute to our core company KPI framework, including its driver tree and sensitivity model
- Contribute analysis to planning and points allocation
- Define certified metrics and resolve conflicting definitions with partners
- Build KPI trees and decompositions that connect product and business drivers to MAU, retention, ARPU, revenue, and margin
- Model KPI drivers and quantify the direction, sensitivity, predictive strength, and time horizon of KPI-to-North-Star relationships
- Distinguish arithmetic identities, predictive associations, and causal effects
- Build scenarios and translate company goals into workstream targets
- Apply established observational causal methods with diagnostics and robustness checks, making identification assumptions explicit
- Build reproducible diagnostics and analytical tools grounded in certified data
- Evaluate the accuracy, adoption, and sustained value of what you build
- Build reproducible, tested analytical workflows in SQL and Python
- Partner with Data Engineering on gold tables, data marts, and semantic layers
- Partner with cross-functionally with teams such as Product and Engineering to drive adoption
- Make actionable recommendations and communicate sensitivity, assumptions, and uncertainty clearly
Minimum Qualifications:
- 5+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments
- Experience working with multiple stakeholders to define KPIs and their hierarchy
- Ability to structure a complex space into a tackle-able project plan
- Track record of establishing trusted metric definitions and driving adoption across Product, Finance, Commercial, and Engineering
- Experience building and validating models that connect user behavior and business drivers to retention, monetization, or company outcomes
- Ability to show sensitivity estimates, leading indicators, time horizons, and out-of-sample predictive performance
- Experience using models in target setting or scenario planning while distinguishing prediction from causal impact
- Experience with ML modeling, including regression, causal inference methodology, and graph models
- Experience modeling multi-metric relationships
- Strong skills in SQL, Python, statistics, causal inference, and forecasting
- Track record of shipping reusable dashboards, diagnostic apps, or analytical frameworks with evidence of accuracy, adoption, and decision impact
- Experience building robust SQL/Python analytical assets and partnering on productionization
- Track record of documenting and sharing reusable methods
- Understanding of experimentation methodology sufficient to identify patterns, concerns, and frameworks that support product data scientists
Preferred Qualifications:
- Consumer product analytics experience connecting acquisition, activation, engagement, retention, and monetization
- Experience owning KPI frameworks, causal measurement, or business planning
- Experience in centralized or platform data science
- Experience with experimentation platforms
- Experience with governed self-service analytics
This is a full-time role that can be held from one of our US offices or remotely in the United States.
Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. The base salary range for this position is $159,945 - $188,171. Discover our benefits and how our employees live rewarded at
https://fetch.com/careers.
— Sarah J.
·
Marketing Manager
★★★★★
Verified
