Contribute to the design, build, and maintenance of data pipelines that ingest player, game, and marketing data from databases, event streams, and external APIs.
Assist in developing, evaluating, and iterating on machine learning models for use cases such as LTV prediction, churn forecasting, player segmentation, and marketing optimization.
Build and optimize analytical datasets and feature pipelines that support modeling, experimentation, and reporting.
Partner with Product, Game Design, Marketing, and Analytics teams to help frame business questions, define success metrics, and surface actionable insights.
Support data quality, reliability, and reproducibility through testing, documentation, and version control.
Help integrate model outputs into dashboards, reporting tools, or downstream systems to support operational decision-making.
Continuously learn and improve your own analytical workflows, modeling approaches, and data tooling under the guidance of senior team members.

Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent professional experience.
1–3 years of professional (non-academic) experience in a data, analytics, or engineering role.
Proficiency in Python for data analysis and modeling, with some exposure to working with RESTful APIs.
Working knowledge of statistical methods and machine learning techniques applied to real-world datasets.
Solid SQL skills and experience querying large datasets; familiarity with cloud data warehouses (e.g., Snowflake, BigQuery, or Redshift) is a plus.
Some exposure to data transformation or orchestration tools (e.g., dbt, Airflow) is a plus but not required.
Willingness to independently own tasks and work through ambiguous problems, with support from the broader team.
Clear communication skills with the ability to explain analytical findings to both technical and non-technical stakeholders.

Data Developer

Key skills