Overview
On Site
Full Time
Skills
QuickBooks
Strategic Alliance
Analytical Skill
Product Engineering
Finance
Marketing
Data Science
Coaching
Teaching
Go-To-Market Strategy
Payments
Product Innovation
Product Marketing
Analytics
Roadmaps
Google Tag Manager (GTM)
Decision-making
Job Details
The Global Business Solutions Group (GBSG) is building a connected end-to-end platform that small and mid-market businesses rely on to run and grow their revenue and profitability. At the heart of this mission, the QuickBooks Money team is developing innovative tools to help businesses get paid fasterand more confidently.
To support this vision, the Payments Product Data Science team delivers actionable insights, strategic partnership, and advanced analytical models to fuel decision-making across Product, GTM, and Commercial strategies. We work cross-functionally with Product, Engineering, Finance, Marketing, and Design to turn data into impact.As a Manager 2 on the Product Data Science team, you will develop and lead a team responsible for unlocking actionable insights and making recommendations that will influence the company's strategic decisions, especially with building products that solve small & mid-market business customers' biggest pain point of getting paid. The ideal candidate will have a strong background in setting a clear vision, building a high performance culture through coaching/teaching and driving winning results with analyses and models informing key Product and Go-to-market decisions.
Responsibilities
Lead a team of 3-5 Data Scientists to deliver high-impact product insights and models. Drive strategic decisions for the Payments ecosystem by uncovering trends, identifying opportunities, and translating data into recommendations. Define and track success metrics tied to business outcomes and customer impact. Build predictive models, segmentation frameworks, and advanced experimentation methods to inform product innovation. Partner with Product, Marketing, Engineering, and Analytics teams to influence roadmap and GTM strategies. Communicate complex findings clearly to technical and non-technical stakeholders. Champion a high-trust, data-driven team culture that values curiosity, experimentation, and continuous learning. Prioritize work for maximum impact, define decision-making frameworks, and remove barriers to execution.
To support this vision, the Payments Product Data Science team delivers actionable insights, strategic partnership, and advanced analytical models to fuel decision-making across Product, GTM, and Commercial strategies. We work cross-functionally with Product, Engineering, Finance, Marketing, and Design to turn data into impact.As a Manager 2 on the Product Data Science team, you will develop and lead a team responsible for unlocking actionable insights and making recommendations that will influence the company's strategic decisions, especially with building products that solve small & mid-market business customers' biggest pain point of getting paid. The ideal candidate will have a strong background in setting a clear vision, building a high performance culture through coaching/teaching and driving winning results with analyses and models informing key Product and Go-to-market decisions.
Responsibilities
Lead a team of 3-5 Data Scientists to deliver high-impact product insights and models. Drive strategic decisions for the Payments ecosystem by uncovering trends, identifying opportunities, and translating data into recommendations. Define and track success metrics tied to business outcomes and customer impact. Build predictive models, segmentation frameworks, and advanced experimentation methods to inform product innovation. Partner with Product, Marketing, Engineering, and Analytics teams to influence roadmap and GTM strategies. Communicate complex findings clearly to technical and non-technical stakeholders. Champion a high-trust, data-driven team culture that values curiosity, experimentation, and continuous learning. Prioritize work for maximum impact, define decision-making frameworks, and remove barriers to execution.
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