Director, Product Analytics

Product Data Science Leader, Fin

What makes this role different

This is not a classic experimentation-first product data science leadership role.

Fin is a fast-moving, ambiguous, B2B AI-native product company. The role is less about owning a neat experimentation roadmap and more about helping shape direction in an environment where product bets are evolving quickly, the operating model is constantly changing, and decisions often need to be made before the data is complete.

This person will not be successful if they wait to be asked for analysis. We need someone who creates momentum, brings clarity to messy problems, and helps leaders decide what matters, where to focus, and what should change.

This is a highly consultative, influence-heavy leadership role. It sits at the intersection of product strategy, product analytics, customer outcomes, go-to-market signals, technical feasibility, and organizational design. Success depends on building trust and traction with product, engineering, design, research, sales, and executive stakeholders, often without relying on formal authority alone.

The role is as much about shaping the system around product data science as it is about analytical depth. A major part of the job is creating the conditions for the function to be effective: improving how decisions get made, clarifying where data science should engage, helping define interfaces with adjacent functions, and ensuring insights actually influence outcomes.

Vision for Product Data Science at Fin

This role is not only about leading the current team well. It is also about helping define what product data science should become in an AI-native product organization.

Fin is building zero-to-one products in an environment where the nature of the work is constantly shifting. As a result, the shape of product data science cannot be static or tied too closely to a traditional experimentation-and-dashboards model.

We expect this leader to help define the future makeup of the function. That includes understanding where we need data scientists who are closer to engineers and builders, where we need people who operate more like researchers, and where deep statistical and analytical rigor should remain central.

This person should bring a clear point of view on what an AI-native product data science function looks like, how AI should change the practice of analysis, and what capabilities, foundations, and operating model are required for the function to have the most impact over time.

They should help define:

• the right capability mix for the team over time
• where foundations work belongs and how it should be prioritized
• how AI can increase leverage in analysis without lowering quality or rigor
• how product data science should evolve as Fin ev