Perfect for those who aim to:
- Analyze player behavior across the full funnel: registration, first deposit, retention, reactivation, churn - and translate findings into actionable product recommendations;
- Design, run, interpret A/B tests and quasi-experiments, handle the methodology for measuring feature impact;
- Build and maintain cohort-based analyses (LTV, retention curves, payback) to support product and investment decisions;
- Define, validate, and monitor product KPIs and metric trees, challenge metrics that don't reflect real business value;
- Partner with product managers to prioritize the roadmap based on expected impact, not intuition;
- Develop self-service dashboards (Tableau) and data sources to help stakeholders answer routine questions without analyst involvement;
- Investigate anomalies in key metrics and communicate root causes clearly to non-technical audiences;
- Maintain culture of rigorous, honest analytics: document assumptions, quantify uncertainty, and flag when data can't answer the question;
- Own web analytics implementation and data quality: tracking plans, event taxonomy, tag management (GTM), and validation of new tracking releases.
Experience you’ll need to bring:
- 2+ years in Product/Web/ or Data analytics;
- Strong SQL - window functions, CTEs, large datasets (Athena or similar is a plus);
- Hands-on experience with web analytics platforms (GA4, Amplitude, Mixpanel, or similar), including event design and tracking implementation;
- Working knowledge of Google Tag Manager or a comparable tag management system and ability to read dataLayer specs and debug tracking issues;
- Solid grounding in statistics: hypothesis testing, confidence intervals, statistical power, common experiment pitfalls;
- Experience with funnel analysis, cohort analysis, LTV and retention modeling;
- Proficiency with a BI tool (Tableau preferred), including data source design;
- Understanding of attribution models and their limitations;
- Python or R for ad-hoc analysis is a plus (pandas, curve fitting, survival analysis);
- English at least at Intermediate level (written and spoken);
- Experience with iGaming, or another high-frequency B2C domain (fintech, gaming, e-commerce) will be a plus.
It's a perfect match if you have those personal features:
- Business-first mindset: you start from the decision that needs to be made, not from the data that happens to be available;
- End-to-end thinking: you naturally connect a landing page tweak to its downstream effect on deposits and LTV;
- Intellectual honesty: you say "the data doesn't support this" even when the room wants a different answer;
- Attention to data quality: you treat broken tracking as a first-priority incident;
- Clarity over sophistication: you prefer a simple, explainable metric that stakeholders trust;
- Ownership: you follow a question from raw event to business decision without waiting for a perfectly specified task.