Product Manager, Advanced Insights & Modeling

Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams — People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more — provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block.

The Role

Block's Advanced Insights and Modeling (AIM) team is an intellectually diverse group of machine learning modelers, data scientists, former strategy consultants, and data engineers. We apply cutting-edge AI, graph analytics, and machine learning to solve high-impact business problems for Block's executive leadership.

Within this team, the Product group owns the end-to-end lifecycle of predictive models that serve stakeholders across Block — including Finance, Marketing, Sales, Legal, and Treasury. The product manager partners closely with Machine Learning engineers (MLE) to scope problems and data strategy and ensure successful model development, validation, deployment, and adoption. We continuously monitor model performance, share results and insights with business partners, and iterate to keep our tools trusted and relevant across a diverse set of use cases.

We're looking for a strategic, analytically rigorous product leader who can operate at 10,000 feet while diving deep into the data. This role blends traditional product management with hands-on data science — you'll frame the business problem, shape the data strategy, and partner with ML engineers to deliver from conception to adoption.

What You'll Do

Problem Framing & Product Strategy

• Own the forecasting product roadmap end-to-end — from problem definition through launch and iteration
• Define business context, success metrics, and constraints that keep work aligned with strategic goals
• Serve as the source of truth for model decisions; maintain clear documentation as systems evolve
• Manage project flow: prioritization, timelines, dependencies, and cross-functional alignment

Data & Signal Engineering

• Partner with ML engineers to ensure models are grounded in a clear, well-articulated understanding of the data
• Conduct in-depth data analysis to inform model design — segmentation, feature engineering, and evaluation strategy
• Leverage AI tools to explore data, generate hypotheses, accelerate analysis, and synthesize insights
• Analyze forecast results to identify trends, variances, and actionable recommendations for model performance

Stakeholder Communication & Adoption

• Translate complex