Data Intelligence Analyst
Job Description
About the Role
We are not looking for someone who just measures numbers — we’re looking for someone who understands why the numbers move. As our Data Intelligence Analyst, you will bridge data science, behavioral psychology, and design, uncovering the cause-and-effect patterns that drive user growth, retention, and monetization.
Key Responsibilities
• Causal Insight Generation – Design and conduct causal analyses (A/B tests, quasi-experiments, and observational studies) to understand the drivers behind player engagement, retention, and revenue.
• Behavioral Analysis – Decompose player journeys using causal frameworks (DAGs, counterfactual thinking, intervention modeling) to pinpoint what actions create lasting value.
• Experimentation Partner – Work with product, growth, and live ops teams to design experiments that identify true lift, not just correlation.
• Strategic Storytelling – Translate complex causal findings into clear narratives and growth strategies. Help leadership move from “what happened” to “why it happened” and “what to do next.”
• Cross-Functional Collaboration – Partner with marketing and product to test hypotheses, evaluate new features, and optimize player funnels through data-driven interventions.
• End-to-End Ownership – From hypothesis framing → data modeling → interpretation → communication — you’ll own the complete analytical lifecycle.
What We’re Looking For
• 1-5 years of experience in data analytics, business intelligence, or product analytics.
• Strong command of SQL and experience with analytical scripting languages (Python, R, or similar).
• Practical understanding of causal inference concepts — such as experiments, control groups, confounders, or counterfactual reasoning — even if you haven’t formally studied them.
• Experience designing or evaluating A/B tests, incrementality studies, or campaign lift analyses.
• Ability to think in causal graphs (DAGs) — not just KPIs — to explain why metrics move.
• Strong communication and data storytelling skills: you can translate causal findings into clear product or growth actions.
• Curiosity-driven mindset: you instinctively ask “why” before “what.”
Nice to Have
• Experience in gaming, social platforms, or consumer entertainment analytics.
• Familiarity with causal inference libraries (DoWhy, EconML, CausalML, PyMC, or similar).
• Knowledge of AARRR metrics (Acquisition, Activation, Retention, Revenue, Referral) and how causal drivers affect each stage.
• Understanding of player psychology and in-game economy dynamics.
• Exposure to lift measurement, churn prediction, and viral loop modeling.
Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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