Product/Data Analyst (Mobile)

About us

We are a small remote team building a Unity mobile game. We run UA through AppLovin Axon, Meta, and Google Ads. Our current MMP is Adjust, and we plan to move to Singular. Experiments and analytics are handled in Firebase. We store data in BigQuery, use Metabase for reporting, and can connect BigQuery to Looker Studio if needed.

What are you working on?

• Genres: Casual
• Platforms: Mobile

For which tasks (responsibilities)?

We’re looking for someone who enjoys digging into player behavior and making data-driven decisions. You will own hypotheses, experimentation, and applied ML (churn, early LTV, segmentation). Dashboards and the reporting layer are supported by the team, so you can focus on analysis and impact.

Role mission

Help the product team understand which changes truly impact retention, churn, user acquisition quality, and monetization, and identify behavioral player segments using data mining and ML.

How we work with hypotheses

We prioritize validation over abstract ideation. We value your input on hypothesis feasibility, but the roadmap is primarily driven by the Product Owner. We want a strong outside perspective: help discuss options, estimate potential impact, risks, and the cost of validation, and together choose which hypothesis is most worth building and how to measure it correctly.

What you will do

Hypotheses, metrics, and change impact

- Refine hypotheses into measurable statements: which metric should move, for whom, and over what time window

- Help decide what to test first by estimating expected impact, confidence, and implementation cost

- Design measurement for product changes: events, audiences, time windows, and guardrail metrics

- Evaluate impact: Firebase A/B tests, holdouts, correct interpretation of results, and control for channel and geo skews

- Deliver clear conclusions for PO and PM: what’s proven, what needs re-validation, and what to do next

Player behavior and segmentation

- Cohort analysis and breakdowns by acquisition sources (Axon, Meta, Google), geo, devices, and versions

- Churn drivers: pre-churn patterns, friction points, and aha moments

- Segment players by play style, progression, ad sensitivity, and purchase propensity

Applied ML and data mining

- Churn prediction and early value estimation (early LTV)

- Clustering and segment identification with practical recommendations for product and marketing

- Association rules and relationship mining: which behavior combinations lead to retention, purchase, or churn

- Survival analysis to model time to churn and risk factors

Data and quality

- Pull data from BigQuery and build datasets for analysis and modeling

- Validate metrics and event quality, and help improve event tracking and metric definitions

- Automated exports to Google Sheets or Excel exports, or CSV dumps are sufficient for the start, but they must be clean and self-explanatory: clear column names, definitions, units, time period, filters, and sources.

Important: we handle visualization and BI smart reporting layer internally.

You are not expected to maintain the reporting layer and dashboards as a separate routine. You can deliver results via Excel reports and short write-ups, while the team supports Metabase and presentations. If helpful, we can connect Looker Studio to BigQuery.

What kind of professional are we looking for?

Position Requirements

Must have:

- Strong SQL and hands-on experience with BigQuery

- Product analytics skills: cohorts, retention, funnels, segmentation, and metric interpretation

- Experimentation and causality: A/B test design, bias awareness, and correct conclusions

- Python for analytics and ML: pandas, numpy, scikit-learn, model evaluation and interpretation

- Ability to turn data into decisions: recommendations, prioritization, and clear tradeoffs/limitations

- Clear communication and documentation: readable Excel reports, metric glossary, reproducible analyses

Strongly preferred:

- Decision trees and gradient boosting: XGBoost, LightGBM, CatBoost

- Clustering: K-Means, GMM, Hierarchical, HDBSCAN

- Association rules: Apriori, FP-Growth

- Survival analysis: Cox, Weibull, discrete-time hazard models

Nice to have:

- Mobile games experience, especially retention, ads, IAP, and LTV

- Understanding of attribution: Adjust now, moving to Singular

- Experience with Firebase events and BigQuery export

- Experience using Metabase and Looker Studio as a data consumer

- Anomaly detection for metrics and traffic

Expected deliverables at the start:

- Excel reports with clear columns and a metric glossary

- A short note per report: what was tested, conclusion, limitation, next step

- When needed: BigQuery SQL queries and notebooks in a repository

What success looks like in the first 4 to 8 weeks:

- Aligned definitions for key metrics and calculation rules

- A map of churn and retention drivers by segments and channels

- A hypothesis selection process: impact, confidence, and validation cost

- A measurement plan for upcoming product changes and live ops

- A first prototype model (churn or early LTV) with an honest quality assessment