Product Data Analyst
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.
- 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.
