Director, Product Analytics

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.

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.

• 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
  

• 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

• 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
  

• 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

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.

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%.
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents.
• Flexible paid time off policy.
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones.
• If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too.
• MacBooks are our standard, but we also offer Windows for certain roles when needed.

#LI-Hybrid