Associate Data Developer
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Requirements
• 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. • Exposure to deploying or productionizing machine learning models in any capacity. • Familiarity with MLOps concepts such as model versioning, monitoring, or retraining. • Background in digital marketing analytics, attribution, or performance optimization. • Experience working with third-party analytics or attribution APIs (e.g., AppsFlyer or similar). • Games industry experience and a passion for games.
Responsibilities
• 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.