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 is used in analysis, insight generation, and decision support, while maintaining a high bar for rigor and judgment

Improve visibility into product performance, opportunity size, and business impact

Partner across product, engineering, design, research, sales, and leadership to connect product signals with customer and commercial outcomes

Build trust with senior stakeholders and challenge weak logic or fuzzy thinking when needed

Help design the operating model for product data science in Fin, including interfaces with analytics engineering, research, and go-to-market teams

Ensure the function builds the right foundations for evaluating and improving zero-to-one AI products

Coach and raise the bar for product data science work through prioritization, judgment, and leadership presence

Ensure scarce data science capacity is focused on the most important problems, not just the loudest requests

What we’re looking for

Strong product data science leadership experience in complex product environments

Track record of shaping strategy, not just supporting execution

Comfort operating in ambiguity and making high-quality judgment calls with imperfect data

Ability to influence senior stakeholders and challenge constructively when needed

Strong analytical depth and technical skills

A strong point of view on what product data science should look like in an AI-native company

Experience evolving team shape and capability mix in response to changing product and organizational needs

Ability to distinguish when the work calls for builder-type data scientists, research-oriented profiles, or deeper statistical specialization

Strong judgment on how AI should and should not be used in analytical and data science work

Excellent prioritization instincts and ability to identify high-leverage work

Experience working across product, engineering, design, research, and commercial stakeholders

Ability to translate across functions and connect product decisions to customer and business outcomes

Strong leadership presence: credibility, judgment, communication, and follow-through

Experience building or evolving operating models, team scope, or cross-functional ways of working

Ideally, experience in AI-native, SaaS, or fast-moving B2B product contexts

What success looks like

Product and R&D leaders actively rely on this person to shape priorities

Data science is involved early enough to influence direction, not just validate decisions late

Teams have a clearer view of product performance, opportunity size, and where intervention is needed

Product data science effort is focused on the highest-leverage opportunities

The interfaces between product data science and adjacent functions are clearer and more effective

The function becomes embedded in how critical Fin decisions get made

The overall quality of decision-making rises around the teams this person supports

The team evolves toward the right mix of capabilities for an AI-native product environment

AI is used to increase leverage in analysis and decision support without lowering rigor

Why this role matters now

Fin is operating in a space where product ships incredibly fast, decisions move quickly, ambiguity is high, and the organization needs stronger clarity on where product data science can have the most impact.

We need a leader who can help shape the agenda, improve the system and operating model, and ensure product data science meaningfully changes outcomes.

Benefits

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us!

Competitive salary and equity in a fast-growing start-up.

We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen.

Regular compensation reviews - we reward great work!

Pension scheme & match up to 4%.