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Animoca Brands is seeking a Lead Data Analyst to drive business decisions through data-driven insights. This is fundamentally a business role, secondarily a technical one. The company excels at user acquisition but faces retention challenges—weekly active users (WAU) and repeat usage rates are below target. You will diagnose and quantify this gap, and your success will be measured by whether the company takes materially different business actions based on your work.
You will sit at the intersection of product, growth, and go-to-market strategy, serving as the single source of truth for business status, root-cause analysis, and response strategies. In a competitive space, the fastest-learning team wins, and you will be the engine driving that learning.
Key responsibilities include:
- Owning core metrics (activation rate, D1/D7/D30 retention, WAU, cohort behavior, CPA by channel/market, leading indicators for transaction and subscription conversion) and building company-wide trust in the data.
- Performing diagnostic analysis: transforming raw product, billing, and engagement data into clear, actionable business recommendations. Identify key retention drivers, high-converting onboarding steps, specialists and verticals that build habit, and where investment yields returns.
- Establishing measurement frameworks for major launches (3D World of Minds interface, native app, account-level subscriptions) with clear success metrics and quantified impact analysis.
- Collaborating with GTM and vertical product managers to assess market opportunity, set targets, and run closed-loop experiment-and-learn cycles to decide where to double down or cut losses.
- Building regular reporting cadence (daily/weekly) for leadership and lightweight self-service dashboards so teams can answer routine questions independently, freeing your time for decision-changing deep analysis.
- Translating analysis into action: tell the data story in ways product, growth, leadership, and the board can directly act on (written and in meetings), with clear recommendations, "so what" clarity, and confidence levels.
- Applying AI across the data stack (querying, summarization, prototyping, automation) to achieve "one analyst outperforming a data team" efficiency.
Requirements:
- Deep business thinking: approach problems through unit economics, payback period, cohort analysis, and lifetime value (LTV). Provide decisive recommendations, not just charts.
- Mastery of SQL and product/behavioral analytics tools; proficiency in modern data stack; fluency in at least one scripting language (Python or equivalent) for deep analysis.
- Proven track record leading retention, growth, or monetization analysis in consumer products (ideally mobile, gaming, subscription, or high-frequency categories), with results that materially changed business decisions.
- AI-native working style: skilled with AI tools to accelerate analysis, with clear judgment about where model outputs are trustworthy and where they are not.
- Sharp instinct for which questions are worth answering; rigorous commitment to truth, willing to surface uncomfortable conclusions.
- Exceptional communication (core competency, not a nice-to-have): distill complex analysis into single, clear recommendations; make non-technical audiences truly understand; write summaries executives can absorb in 2 minutes; defend your position with data under scrutiny. If the business doesn't act on your work, the analysis didn't land—that's the analyst's responsibility.
Bonuses:
- Familiarity with gamification metrics (DAU/WAU/MAU, game progression, quest/level completion) and A/B testing/experimentation frameworks.
- Knowledge of subscription and payment data (e.g., Stripe) and transaction fee economics.
- Experience with agentic AI, consumer AI, or creator economy products.
