Director, Product Analytics

Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences.

Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey – from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk for one single system that is designed to meet the needs of modern day support teams.

Founded in 2011, Fin became one of the fastest growing companies and remains one of the largest private software companies in the world with nearly 30,000 global businesses using our products to transform their customer support. Driven by our core values, we push boundaries, build with speed and intensity, and relentlessly deliver incredible value to our customers.

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 evolves.

What this role is really about

• Bringing clarity to ambiguous product bets with data and insights
• Helping shape strategy in fast-moving B2B AI product areas
• Pushing for better decisions, not just better analysis
• Identifying product and performance gaps early;
• Influencing where the organization should act
• Creating traction for product data science where the model is still evolving
• Designing how product data science should work, not just delivering within the current setup
• Redirecting effort toward the highest-leverage problems
• Leading with judgment, influence, credibility, and conviction

What you’ll do

• Help product and company leaders make better decisions on where Fin should focus
• Bring structure and judgment to ambiguous product, customer, and performance questions
• Identify what is and is not working in the product, and where intervention is most needed
• Shape early product direction, especially in zero-to-one and fast-evolving areas
• Define where product data science should engage deeply versus where lighter support is sufficient
• Define a vision for what product data science should look like in an AI-native product organization
• Shape how AI …