Senior Data Scientist II, Core & Agentic Automation

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 establish governed self-service and agentic workflows that reduce bespoke work and improve decision speed while maintaining scientific quality.

You'll partner with DS leadership to prioritize analytical investments, and independently lead the technical approach, delivery, and adoption of cross-team KPI, measurement, and automation workstreams.You'll shape leadership strategy, align Finance and Product on targets and trade-offs, and mentor scientists, partnering with cross-functional partners along the way.

This is a full-time role that can be held from one of our US offices or remotely in the United States.

Role Responsibilities:

• Define core data science priorities across teams, deciding which questions and investments to pursue or decline
• Help deliver our core company KPI framework, including its driver tree and sensitivity modeling
• Lead reusable, company-wide analytical frameworks and automation
• Personally design, build, evaluate, and maintain agentic analytical workflows that use certified metrics to investigate KPI changes, generate evidence-backed hypotheses, and support follow-up analysis, with monitoring, failure handling, and human review where needed.
• Design reusable frameworks with evaluation and evidence traceability
• Set definitions and targets for certified metrics with owners, and resolve conflicting definitions with partners
• Build KPI trees and decompositions that connect product and business drivers to MAU, retention, ARPU, revenue, and margin
• Choose defensible models and quantify the direction, sensitivity, predictive strength, and time horizon of KPI-to-North-Star relationships
• Validate leading indicators out of sample, distinguishing arithmetic identities, predictive associations, and causal effects
• Build scenarios and defensible team targets
• Set measurement standards and defend identification assumptions, guiding decisions when randomization or causal evidence is limited
• Select observational methods when needed, using diagnostics and robustness checks
• Measure accuracy, adoption, reduced bespoke analysis, and time to decision
• Build reproducible, tested analytical workflows in SQL and Python
• Partner with Data Engineering on gold tables and data marts
• Shape leadership strategy and team accountability, aligning Finance and Product on targets, trade-offs, and when evidence warrants a change
• Partner with Product, Finance, Commercial, and Engineering to drive adoption
• Communicate assumptions and uncertainty clearly
• Mentor scientists and establish reusable KPI, measurement, and modeling standards adopted across teams

Minimum Requirements:

• 8+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments
• Ability to explain which questions and investments you prioritized or declined, and how quantified KPI relationships changed planning or investment decisions
• Proven ability to define KPIs and their hierarchy with multiple stakeholders, structure complex spaces into tackle-able plans, and establish trusted metric definitions adopted across Product, Finance, Commercial, and Engineering
• Track record of aligning Finance and Product on targets, trade-offs, and when evidence warrants a change
• Experience selecting and validating statistical, predictive, and causal models for business planning, including regression, forecasting, sensitivity analysis, and uncertainty estimation
• Skilled at using models for target setting and scenario planning while distinguishing prediction from causal impact
• Demonstrated ability to set measurement standards and defend identification assumptions when randomization or causal evidence is limited, with enough experimentation knowledge to support product data scientists
• Strong skills in SQL, Python, statistics, causal inference, and forecasting
• Track record of shipping reusable dashboards, diagnostic apps, or analytical frameworks with proven accuracy, adoption, and decision impact, and partnering on productionization of robust SQL/Python assets
• History of mentoring scientists and establishing KPI, measurement, or modeling standards that raise quality across teams
• Hands-on experience automating systems with AI, including personally building and shipping agentic automation used in real analytical or business workflows, whether in production or with internal users
• Ability to explain your contribution and how the system retrieves data, uses tools, and completes tasks
• Proven approach to evaluating agentic systems for numerical and analytical accuracy, traceability, failure handling, and adoption, and reusing that approach beyond a single demo or one-off analysis

Preferred Requirements:

• Consumer product analytics experience connecting acquisition, activation, engagement, retention, and monetization
• Experience owning KPI frameworks, causal measurement, or business planning across multiple teams
• Experience in centralized or platform data science
• Experience with governed self-service analytics