Monitor all running A/B tests to ensure data quality and experiment health
Analyse experiment results end-to-end — understand what drove the change, why users dropped off, and what it means for the product
Co-generate hypotheses with designers, PMs, and engineers — helping build a strong experimentation pipeline
Evaluate feature viability — help the team decide whether a feature is worth investing in, backed by data
Proactively analyse user behaviour to uncover growth opportunities, friction points, and product risks before they become obvious
Translate data into clear product recommendations: what to improve, what to stop, what to scale, and what to test next

Strong analytical skills and critical thinking — you question assumptions and challenge weak conclusions
Solid understanding of statistical analysis and A/B testing principles
Strong communication skills — ability to translate complex data into clear insights and align people around a direction
Fast learner with a strong bias for results
Proficiency in SQL and BI tools (Tableau)
Experience with user behaviour analytics tools — Amplitude, GA4, PostHog, or similar
Python skills for deeper analysis and automation will be a plus
Experience in a startup or fast-growing team will be a plus

Data Analyst

Key skills