Senior Data Scientist II, Core & Agentic Automation

Fetch•US - Remote,

1d•$183,665 - $216,077

About The Position

The Senior Data Scientist II will help Fetch maintain rigorous performance standards and scalable self-service analytics. The role contributes to the core company KPI framework and its underlying model, while leading reusable, company-wide analytical frameworks and automation. The work focuses on trusted metrics: standardizing how impact is measured and connecting product and business drivers to outcomes such as MAU, retention, ARPU, revenue, and margin. The role establishes governed self-service and agentic workflows that reduce bespoke work and improve decision speed while maintaining scientific quality. The position defines core data science priorities across teams and sets KPI, measurement, and modeling standards adopted by others. It shapes leadership strategy, aligns Finance and Product on targets and trade-offs, mentors scientists, and partners with cross-functional teams. The role can be based at a US office or remotely in the United States.

Requirements

At least 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 were prioritized or declined, and how quantified KPI relationships changed planning or investment decisions.
Ability to define KPIs and their hierarchy with multiple stakeholders, structure complex spaces into actionable 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.
Deep expertise building and validating ML models, including regression, causal inference, and graph models, that connect user behavior and business drivers to retention, monetization, or company outcomes.
Skill using models for target setting and scenario planning while distinguishing prediction from causal impact.
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, in production or with internal users.
Ability to explain personal contributions 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.

Nice To Haves

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 experimentation platforms.
Experience with governed self-service analytics.

Responsibilities

Define core data science priorities across teams, deciding which questions and investments to pursue or decline.
Help deliver the core company KPI framework, including its driver tree and sensitivity modeling.
Lead reusable, company-wide analytical frameworks and automation.
Establish governed self-service and agentic workflows that reduce bespoke work and improve decision speed while maintaining scientific quality.
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 connecting 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.