Work as a Data Scientist within a dedicated AI/ML operations environment, shaping the development and deployment of intelligent systems that directly influence gameplay and live operations. Your core responsibility will be designing, implementing, and maintaining machine learning models and the pipelines that support them, using extensive internal datasets to generate actionable outcomes.
Key Responsibilities
- Develop and refine machine learning models and training workflows by extracting and engineering features from large, complex datasets.
- Collaborate with product, live ops, and technical teams to identify business challenges and deliver data-driven solutions.
- Support the architecture and implementation of AI-powered services, contributing to their full lifecycle from concept to production.
- Build interactive internal tools and dashboards that allow non-technical users to access and interpret model outputs effectively.
Qualifications and Experience
Applicants should have at least three years of experience in data science or machine learning engineering roles, with a demonstrated ability to apply ML techniques to real-world problems. Proficiency in Python and the surrounding data science stack is essential, along with hands-on experience managing large-scale structured and unstructured data.
A solid grasp of model development, deployment, and monitoring processes is required. Fluency in English is necessary for effective collaboration across teams.
Preferred Background
- Advanced degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
- Experience in deep learning for image analysis or generation, time series forecasting, or natural language processing.
- Familiarity with Snowflake as a data warehouse and machine learning environment.
- Proficiency with data visualization platforms such as Tableau or Power BI, or Python frameworks like Dash and Streamlit.
- Working knowledge of AWS services including SageMaker, EC2, Fargate, and ECS.
- Experience with Docker, Git, and CI/CD practices in ML workflows.
