Senior Data Scientist II, Core & Agentic Automation

OpenKrill; USA; Remote; USD 180.000 - 240.000; Vollzeit

Zusammenfassung

Fetch is building a world where insights power more rewarding customer experiences. We seek a senior data scientist to drive KPI development, design scalable analytics, and mentor a team of scientists.

You will work with product, finance, and engineering to align targets and measure impact through robust ML models and dashboards. You will lead cross-functional data science priorities, establish standard measures, and ensure data-driven decisions across the organization.

Qualifikationen

• 8+ years of experience in data science, machine learning, or applied analytics in product-driven environments.
• Ability to define KPIs and their hierarchy with multiple stakeholders.
• Proven track record of aligning Finance and Product on targets and trade-offs.
• Deep expertise building and validating ML models that connect user behavior to outcomes.
• Strong skills in SQL, Python, statistics, causal inference, and forecasting.
• Experience shipping reusable dashboards or analytical frameworks with measurable impact.

Aufgaben

• Define core data science priorities across teams and decide which questions to pursue.
• Help deliver the core KPI framework and its driver tree and sensitivity modeling.
• Lead reusable, company-wide analytical frameworks and automation.
• Establish governed self-service workflows that reduce bespoke work and speed decisions.
• Design reusable analytical frameworks with evaluation and evidence traceability.
• Build KPI trees linking product drivers to MAU, retention, ARPU, revenue and margin.
• Mentor scientists and establish standards adopted across teams.

Kenntnisse

SQL

Python

Statistics

Causal inference

Forecasting

Jobbeschreibung

What we’re building and why we’re building it.

Fetch helps people live rewarded every day, with a vision to become the rewards destination for everyone. We turn everyday activities into meaningful rewards, whether it’s grocery shopping, grabbing a quick meal, or playing a favorite mobile game. To date, we’ve awarded more than $1 billion in Fetch Points to our users.

Each day, more than 13 million receipts are submitted on Fetch, providing visibility into over $212 billion in gross merchandise value. This creates the largest retail-agnostic, SKU-level view of household spending, powering Fetch as an outcomes-based advertising platform that helps brands acquire and retain lifelong consumers.

The Fetch app is available on the App Store and Google Play, with more than 6 million five-star reviews from a highly engaged and loyal user base.

It’s not just our users who believe in Fetch: with investments from Softbank, ICONIQ, DST, Greycroft, and partnerships ranging from challenger brands to Fortune 500 companies, Fetch is reshaping how brands and consumers connect in the marketplace. When you work at Fetch, you play a vital role in a platform that drives brand loyalty and creates lifelong consumers with the power of Fetch points. User and partner success are at the heart of everything we do, and we extend that same commitment to our employees.

At Fetch, we value curiosity, adaptability, and the confidence to explore new tools, especially AI, to drive smarter, faster work. You don’t need to be an expert, but you should be ready to learn quickly and think critically. We welcome learners who move fast, challenge the status quo, and shape what’s next, with us. Ranked as one of America’s Best Startup Employers by Forbes for two years in a row, Fetch fosters a people-first culture rooted in trust, accountability, and innovation. We encourage our employees to challenge ideas, think bigger, and always bring the fun to Fetch.

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 define core data science priorities across teams and set the KPI, measurement, and modeling standards others adopt. 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.

What You'll Do at Fetch:

• 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
• 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 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 Qualifications:

• 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
• 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
• 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 Qualifications:

• Consumer product analytics expe