Product Analyst - Gaming Industry

What You'll Do (Key Responsibilities):

- Track and analyze the performance of user acquisition (UA) campaigns across GEOs, platforms, and ad networks using mobile attribution tools (AppsFlyer, Adjust, Firebase, SKAdNetwork, Appslovin etc).

- Analyze gameplay telemetry and player behavior to uncover patterns and opportunities for product improvement.

- Build, maintain, and automate dashboards and reports for UA/Product metrics: CPI, ROAS, LTV, retention, payback periods,Churn,User engagement etc.

- Partner with UA & creative teams to evaluate creative variants, run incrementality tests, and analyze cohorts (view-through vs click-through, retention cohorts).

- Develop predictive models to forecast campaign outcomes, optimize budget allocation, and estimate LTV / ROAS across different spends or channel mixes.

- Conduct rigorous statistical analyses-including A/B testing, cohort analysis, regression modeling, and significance testing-to derive actionable insights.

- Set up and refine ETL pipelines to pull raw campaign / attribution / MMP / in-app data into warehouses (BigQuery or similar) for analysis.

- Conduct ad-hoc analyses for UA managers-budget allocation, funnel drop-offs, creative insights, and performance anomalies

- Ensure tracking & attribution setups (UTMs, conversion events, MMP integrations) are correct and QA'd.

- Translate complex data into visual dashboards and clear narratives for marketing and leadership teams.

- Mentor junior team members to help them grow in analytics, data quality checks, and dashboard development.

Mandatory Qualifications

- Bachelor's degree in a quantitative discipline (Statistics, Data Science, Computer Science, or related fields).

- 4 - 6 years in data analysis, with a strong emphasis on predictive modeling, statistical analysis, and UA/growth analytics, ideally within gaming or mobile apps.

- Proficient in experiment design, significance testing, cohort analysis, regression modeling, and predictive forecasting.

- Strong skills in SQL (querying raw data) and experience in Python or R for aggregation, predictive modeling, automation, and statistical analysis.

- Hands-on with attribution tools (AppsFlyer / Adjust / Firebase / SKAdNetwork) and comfortable interpreting metrics like ROAS, CPI, LTV, retention.

- Proficiency in dashboarding / visualization tools (Tableau, Power BI, Looker, or similar).

- Excellent communication skills: ability to explain analytical findings and make recommendations to non-technical stakeholders.