Product Data Analyst

Spain / Cyprus / United Kingdom / Poland / Estonia

At Finom, product decisions are made fast — and we want them made well. As our Senior Product Analyst for Growth & Activation, you'll be the analytical engine behind the squads shaping how new customers discover, adopt, and grow with our product.

You'll sit embedded inside the Growth & Activation product stream, working shoulder-to-shoulder with Product Managers and cross-functional squads, while reporting functionally to the Head of Product Analytics to keep our metrics rigorous and consistent. Your job is to turn data into insight, hypotheses into experiments, and outcomes into learnings that genuinely move the roadmap.

This is a builder's role. The data foundations are there, but the metric system around them is yours to design and document. If you've ever wanted a blank canvas to build an analytical setup the way it should be built — without layers of bureaucracy slowing you down — this is that opportunity.

What You Will Be Doing

• Be the analytical lead embedded across the four product teams in the Growth & Activation stream, partnering closely with PMs to make their decisions faster, smarter, and measurable.

• Translate product hypotheses and business problems into clear, measurable analytical questions and scalable models.

• Design, instrument, and evaluate experiments and A/B tests alongside PMs and Engineers, so every product change can be assessed in real business terms — activation, retention, engagement, or revenue.

• Monitor product performance, funnel dynamics, and behavioral cohorts to surface early signals of impact, degradation, or new opportunity — and proactively challenge assumptions through opportunity sizing and trade-off analysis.

• Build decisioning frameworks — from scoring systems and rules to predictive or optimization models — that directly shape product logic and strategy, prototyping in SQL, Python, or notebooks before partnering with Data Science to productionize.

• Define and maintain success metrics and driver trees for your product area, aligned with our company-wide metric architecture, and automate dashboards for ongoing KPI and experiment visibility.

• Apply statistical and causal methods (e.g., Bayesian estimation, difference-in-differences, synthetic control, uplift modeling) when clean randomization isn't possible.

• Collaborate across functions — Data Science, Growth, Marketing Analytics, Finance, and Engineering — on shared models and KPIs such as LTV, CAC payback, and pricing elasticity, and contribute to company-wide frameworks for activation, retention, and monetization.

Who You Are

• A cross-functional operator. You have a proven track record of managing competing priorities from multiple demanding stakeholders at once, and you're comfortable saying "no" and pushing back when teams pull you in different directions.

• A strong communicator first. You can take ambiguity, structure it, and explain your reasoning clearly to product, engineering, and business partners. We value this above raw tooling skill.

• Technically capable. You're fluent in SQL and comfortable prototyping analysis in Python or notebooks. You don't need to be a production ML engineer, but you should be able to validate feasibility yourself.

• Quantitatively grounded. You hold a Bachelor's or Master's in a natural science, mathematics, or another highly quantitative field, and you have a solid conceptual command of statistics and experimentation.

• A builder with a structural mindset. You're energized by bootstrapping a data culture from a blank slate, you adapt quickly in a fast-moving environment, and you bring order to undocumented systems rather than waiting for someone to hand you a neatly defined task.

Nice to Have

• Experience in high-growth FinTech, or in industries with high-frequency, mobile-first metrics such as gaming/gamedev, digital advertising, or e-commerce.

• Background in banking, finance, or credit/risk analytics.

• Some leadership or mentoring experience.

• Hands-on experience with causal inference, event-path analysis, or ML-based clustering in a product context.