Mid/Senior Data Scientist

Proven Data Experience: At least 3 years of hands-on experience in data analysis, business intelligence, or a related role (exposure to data engineering/analytics engineering is a plus), with a strong understanding of mobile product analytics and KPIs, as well as familiarity with lifecycle metrics (retention, churn, LTV) and how to model or forecast them for decision-making.

SQL & BigQuery Proficiency: Advanced SQL skills and experience with cloud-based data warehouses (especially Google BigQuery), including writing complex, optimized queries on large datasets.

ETL & Dashboard Skills: Solid experience with ETL processes and managing data pipelines; proficiency with business intelligence tools such as Looker, Metabase or Tableau. Experience with modern data workflow frameworks (e.g., Dataform or dbt on GCP) is a strong plus.

Programming Knowledge: Proficiency in Python for scripting, automation, and data analysis tasks. Ability to write code to support ETL pipelines and perform statistical analysis and lightweight modeling (e.g., forecasting, classification, segmentation).

Mobile Analytics Tools: Experience with mobile attribution/analytics platforms like Appsflyer or Adjust is a big plus, especially for campaign tracking and user acquisition analysis.

Analytical and growth-oriented: Strong problem-solving skills with the ability to turn complex data into clear, actionable insights. Comfortable shifting between priorities, from building pipelines and optimizing data models to delivering dashboards, while continuously improving as our analytics setup evolves.

Cross-functional communicator: Comfortable working across departments and proactively sharing insights. Able to present findings clearly to both technical and non-technical audiences, with a structured narrative that drives alignment and action.